Biomedical signal processing and interpretation


Raimon Jané Campos | Group Leader
José Antonio Fiz Fernández | Senior Researcher
Beatriz Giraldo Giraldo | Senior Researcher
Jordi Solà Soler | Senior Researcher
Abel Torres Cebrián | Senior Researcher
Luis Estrada Petrocelli | Postdoctoral Researcher
Manuel Lozano García | Postdoctoral Researcher
Leonardo Sarlabous Uranga | Postdoctoral Researcher
Dolores Blanco Almazán | PhD Student
Javier Rodríguez Benítez | PhD Student
Noelia Vaquero Gallardo | Masters Student

About

The group’s research addresses the design and development of advanced signal processing techniques and the interpretation of biomedical signals to improve non-invasive monitoring, diagnosis, disease prevention and pathology treatment.

Left: Improvement in Neural Respiratory Drive Estimation from Diaphragm Electromyographic Signals usingSample Entropy of non-invasive EMG signals (Estrada et al., 2016, IEEE Journal of Biomedical and Health Informatics).

Our main objective is to improve diagnosis capability through the characterization of physiological phenomena and to enhance early detection of major cardiac and respiratory diseases and sleep disorders. We propose and design new signal processing algorithms and develop new biosignal databases, with the collaboration of our hospital partners. To validate the clinical information of new surface signals, we have developed specific invasive/non-invasive protocols and animal models. The group focuses its research in a translational way to promote the transfer of our scientific and technological contributions. Currently, our prototypes are used in hospitals for research purposes and for future industrial developments.

Right: Novel method for differentiating normal from adventitious respiratory sounds (RS) to improve the diagnosis of pulmonary diseases. Particularly, continuous adventitious sounds (CAS) are of clinical interest because they reflect the severity of certain diseases. The new method is based on the multi-scale analysis of instantaneous frequency (IF) and envelope (IE) calculated after ensemble empirical mode decomposition (EEMD) of respiratory sounds. (Lozano et al., 2016, IEEE Journal of Biomedical and Health Informatics).

Highlights in 2016:

Obstructive Sleep Apnea and Sleep Disorders
– A novel method to analyse cardiorespiratory synchronization in OSA patients during sleep (IEEE-EMBC 2016, 4280-4283) and characterize snores (CASEIB 2016, 531-536), with the Hospital Germans Trias i Pujol, Badalona.

Chronic Obstructive Pulmonary Disease and Asthma
– Automatic detection of continuous adventitious respiratory sounds in asthmatic patients using ensemble empirical mode decomposition and instantaneous frequency (J Biomed Health Inf 2016, 20 (2): 486-497) and Hilbert-Huang transform (Signal Processing 2016, 120:99-116), with the Hospital Germans Trias i Pujol, Badalona (PhD thesis, M. Lozano).
– Non-invasive estimation of neural respiratory drive from diaphragm electromyographic signals using fixed sample entropy (J Biomed Health Inf 2016, 20 (2): 476-485); (PhD thesis, L. Estrada).
– Novel methods to estimate the respiratory muscle activity using wireless sensor platform (IEEE-EMBC 2016, 5769-5772, CASEIB 2016, 244-247 and CASEIB 2016, 556-559).
– Time-frequency representation of the sternocleidomastoid muscle activity during respiratory activity by electromyography recorded with concentric ring electrodes (IEEE-EMBC 2016, 3785-3788), with the Universidad Politécnica de Valencia.

Cardiac and cardiorespiratory diseases and ageing
– Evaluation of Periodic Breathing in Respiratory Flow Signal of Elderly Patients using SVM and Linear Discriminant Analysis (IEEE-EMBC 2016, 4276-4279).
– Estimation of blood pressure in patients with different ventricular ejection fraction using linear and non-linear methods (IEEE-EMBC 2016, 2700-2703) and characterization of patients with cardiovascular risk using Poincaré Plots (CASEIB 2016, 396-399), with Hospital Germans Trias i Pujol, Badalona and University of Jena, Germany.
– Analysis of ECG signal to risk stratification in patients with Parkinson disease (CASEIB 2016, 531-534).

Neurorehabilitation and Biofeedback
– Novel methods for analysis of the interlimb similarity of motor patterns for improving stroke assessment and neurorehabilitation (PhD thesis, O. Urra).

News/Jobs

Screening improvements for asthma and obstructive pulmonary disease patients
13/03/17

Some IBEC research published in PlosOne offers a step towards better screening of patients with asthma and other sufferers of obstructive pulmonary diseases.


Collaboration with clinicians leads to new non-invasive monitoring of COPD
01/02/16

A collaboration between IBEC’s Biomedical Signal Processing and Interpretation group and two local hospitals has resulted in a new non-invasive method of evaluating the efficiency of the respiratory muscles in patients with chronic obstructive pulmonary disease (COPD).


IBEC group leader in new CIBER-BBN steering committee
09/06/15

Biomedical Signal Processing and Interpretation group leader and UPC professor Raimon Jané has been appointed as a member of the new CIBER-BBN Steering Committee.


IBEC hosting CASEIB2014 this week
26/11/14

Today is the first day of the annual congress of the Sociedad Española de Ingeniería Biomédica (CASEIB 2014) which this year being organized by IBEC, as Biomedical Signal Processing and Interpretation group leader Raimon Jané is SEIB’s president. It is taking take place at Barcelona’s CosmoCaixa museum until Friday 28th November.


“Engineering Sleep Disorders”
30/09/14

An article by Raimon Jané appears in this month’s edition of IEEE Pulse, the magazine of the IEEE Engineering in Medicine and Biology Society, which is devoted to the science of sleep.


Sounds of health
10/04/14

A PLOS ONE article by researchers from the joint unit IBEC, the Fundació Institut Germans Trias i Pujol (IGTP) and the Pneumology Service at Germans Trias i Pujol University Hospital describes the acoustic analysis of pulmonary sound intensity as a non-invasive, more objective, easier and cheaper method to improve diagnostics in unilateral diaphragmatic paralysis.


Faster and more accurate testing for causes of lower respiratory tract infections
07/04/14

Research and industry collaboration develops miniature diagnostic platform for respiratory infections such as pneumonia and infectious bronchitis.


Every breath you take
19/02/14

New non-invasive monitoring will help patients with pathologies such as chronic obstructive pulmonary disease (COPD).


Measuring signals at the 11th “Recerca en directe” Science Fair
23/04/2013

IBEC’s Biomedical Signal Processing and Interpretation group took part in this year’s “Recerca en directe” science fair at La Pedrera, where they introduced an activity that helped visitors record and process their own biomedical signals.


“CASEIB 2012 celebrado en Donostia-San Sebastián”
12/03/2013

IBEC group leader Raimon Jané, in his role as president of the Sociedad Española de Ingeniería Biomédica (SEIB), features in a video about the society’s CASEIB 2012 annual meeting in San Sebastián in November.


“Raimon Jané, presidente de la Sociedad Española de Ingeniería Biomédica
01/03/2013

An interview with IBEC group leader Raimon Jané appears in the latest edition of CIBER-BBN’s newsletter.


Presidency of SEIB for IBEC group leader
05/12/2012

Biomedical signal processing and interpretation group leader Raimon Jané was elected as president of the Sociedad Española de Ingeniería Biomédica (SEIB) at its annual meeting (CASEIB) in San Sebastián.


“Separ y Ciber-BBN apuestan por la innovación”
19/06/2012

Last week Biomedical signal processing and interpretation group leader Raimon Jané was an invited speaker at a special institutional joint session of the CIBER-BBN (Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina) and SEPAR (Sociedad Española de Neumología y Cirugía Torácica) at SEPAR’s 45th National Meeting in Madrid.


IBEC signs collaboration agreements with IGTP
05/06/2012

Last week IBEC signed two collaboration agreements with the Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol (IGTP) to carry out and promote scientific and translational research.


“System Automatically Monitors Apnea-Hypopnea Index”
18/04/2011

Some recent research by the Biomedical Signal Processing and Interpretation group has been featured in the section ‘Literature Review: A roundup of the most important recent studies’ in ENT Today magazine.

Projects

National projects
Multimodal invasive and non-invasive biomedical signal interpretation and modelling in cardiac, respiratory and neurological disorders MINECO, I+D-Investigación fundamental no orientada Raimon Jané
M-Bio4Health Biomarcadores fisiológicos multimodales para la monitorización no-invasiva y cuidado a domicilio de pacientes EPOC con comorbilidades MINECO, Retos investigación: Proyectos I+D Raimon Jané
Privately-funded projects
Study on software comparison of audio recordings and correlation to SAHS events Audiodontics LLC Raimon Jané
Novel m-Health tools for unobtrusive sensing and management improving of Obstructive Sleep Apnea patients at home Obra Social La Caixa Raimon Jané
CIBER-BBN Intramural projects
M-OLDOSA Multimodal analysis and m-Health tools for diagnostic and monitoring improving of Obstructive Lung Disease and Obstructive Sleep Apnea patients CIBER-BBN, Spain Raimon Jané
MultiTools2Heart Multiscale computational tools to improve diagnosis, risk assessment and treatment in prevalent heart diseases
CIBER-BBN, Spain Juan Pablo Martínez Cortés

Publications

Estrada, L., Torres, A., Sarlabous, L., Jané, R., (2017). Onset and offset estimation of the neural inspiratory time in surface diaphragm electromyography: A pilot study in healthy subjects IEEE Journal of Biomedical and Health Informatics Epub ahead of print

This study evaluates the onset and offset of neural inspiratory time estimated from surface diaphragm electromyographic (EMGdi) recordings. EMGdi and airflow signals were recorded in ten healthy subjects according to two respiratory protocols based on respiratory rate (RR) increments, from 15 to 40 breaths per minute (bpm), and fractional inspiratory time (Ti/Ttot) decrements, from 0.54 to 0.18. The analysis of diaphragm electromyographic (EMGdi) signal amplitude is an alternative approach for the quantification of neural respiratory drive (NRD). The EMGdi amplitude was estimated using the fixed sample entropy computed over a 250 ms moving window of the EMGdi signal (EMGdifse). The neural onset was detected through a dynamic threshold over the EMGdifse using the kernel density estimation method, while neural offset was detected by finding when the EMGdifse had decreased to 70 % of the peak value reached during inspiration. The Bland-Altman analysis between airflow and neural onsets showed a global bias of 46 ms in the RR protocol and 22 ms in the Ti/Ttot protocol. The Bland-Altman analysis between airflow and neural offsets reveals a global bias of 11 ms in the RR protocol and -2 ms in the Ti/Ttot protocol. The relationship between pairs of RR values (Pearson’s correlation coefficient of 0.99, Bland- Altman limits of -2.39 to 2.41 bpm, and mean bias of 0.01 bpm) and between pairs of Ti/Ttot values (Pearson’s correlation coefficient of 0.86, Bland-Altman limits of -0.11 to 0.10, and mean bias of -0.01) showed a good agreement. In conclusion, we propose a method for determining neural onset and neural offset based on non-invasive recordings of the electrical activity of the diaphragm that requires no filtering of cardiac muscle interference.

Keywords: Kernel density estimation (KDE),, Surface diaphragm electromyographic,, (EMGdi) signal,, Inspiratory time,, Neural respiratory drive (NRD),, Neural inspiratory time,, Fixed sample entropy (fSampEn)


Sarlabous, Leonardo, Torres, Abel, Fiz, José A., Martínez-Llorens, Juana M., Gea, Joaquim, Jané, Raimon, (2017). Inspiratory muscle activation increases with COPD severity as confirmed by non-invasive mechanomyographic analysis PLoS ONE 12, (5), e0177730

There is a lack of instruments for assessing respiratory muscle activation during the breathing cycle in clinical conditions. The aim of the present study was to evaluate the usefulness of the respiratory muscle mechanomyogram (MMG) for non-invasively assessing the mechanical activation of the inspiratory muscles of the lower chest wall in both patients with chronic obstructive pulmonary disease (COPD) and healthy subjects, and to investigate the relationship between inspiratory muscle activation and pulmonary function parameters. Both inspiratory mouth pressure and respiratory muscle MMG were simultaneously recorded under two different respiratory conditions, quiet breathing and incremental ventilatory effort, in 13 COPD patients and 7 healthy subjects. The mechanical activation of the inspiratory muscles was characterised by the non-linear multistate Lempel–Ziv index (MLZ) calculated over the inspiratory time of the MMG signal. Subsequently, the efficiency of the inspiratory muscle mechanical activation was expressed as the ratio between the peak inspiratory mouth pressure to the amplitude of the mechanical activation. This activation estimated using the MLZ index correlated strongly with peak inspiratory mouth pressure throughout the respiratory protocol in both COPD patients (r = 0.80, p<0.001) and healthy (r = 0.82, p<0.001). Moreover, the greater the COPD severity in patients, the greater the level of muscle activation (r = -0.68, p = 0.001, between muscle activation at incremental ventilator effort and FEV1). Furthermore, the efficiency of the mechanical activation of inspiratory muscle was lower in COPD patients than healthy subjects (7.61±2.06 vs 20.42±10.81, respectively, p = 0.0002), and decreased with increasing COPD severity (r = 0.78, p<0.001, between efficiency of the mechanical activation at incremental ventilatory effort and FEV1). These results suggest that the respiratory muscle mechanomyogram is a good reflection of inspiratory effort and can be used to estimate the efficiency of the mechanical activation of the inspiratory muscles. Both, inspiratory muscle activation and inspiratory muscle mechanical activation efficiency are strongly correlated with the pulmonary function. Therefore, the use of the respiratory muscle mechanomyogram can improve the assessment of inspiratory muscle activation in clinical conditions, contributing to a better understanding of breathing in COPD patients.


Lozano-García, M., Fiz, J. A., Martínez-Rivera, C., Torrents, A., Ruiz-Manzano, J., Jané, R., (2017). Novel approach to continuous adventitious respiratory sound analysis for the assessment of bronchodilator response PLoS ONE 12, (2), e0171455

Background A thorough analysis of continuous adventitious sounds (CAS) can provide distinct and complementary information about bronchodilator response (BDR), beyond that provided by spirometry. Nevertheless, previous approaches to CAS analysis were limited by certain methodology issues. The aim of this study is to propose a new integrated approach to CAS analysis that contributes to improving the assessment of BDR in clinical practice for asthma patients. Methods Respiratory sounds and flow were recorded in 25 subjects, including 7 asthma patients with positive BDR (BDR+), assessed by spirometry, 13 asthma patients with negative BDR (BDR-), and 5 controls. A total of 5149 acoustic components were characterized using the Hilbert spectrum, and used to train and validate a support vector machine classifier, which distinguished acoustic components corresponding to CAS from those corresponding to other sounds. Once the method was validated, BDR was assessed in all participants by CAS analysis, and compared to BDR assessed by spirometry. Results BDR+ patients had a homogenous high change in the number of CAS after bronchodilation, which agreed with the positive BDR by spirometry, indicating high reversibility of airway obstruction. Nevertheless, we also found an appreciable change in the number of CAS in many BDR- patients, revealing alterations in airway obstruction that were not detected by spirometry. We propose a categorization for the change in the number of CAS, which allowed us to stratify BDR- patients into three consistent groups. From the 13 BDR- patients, 6 had a high response, similar to BDR+ patients, 4 had a noteworthy medium response, and 1 had a low response.Conclusions In this study, a new non-invasive and integrated approach to CAS analysis is proposed as a high-sensitive tool for assessing BDR in terms of acoustic parameters which, together with spirometry parameters, contribute to improving the stratification of BDR levels in patients with obstructive pulmonary diseases.


Garde, A., Sörnmo, L., Laguna, P., Jané, R., Benito, S., Bayés-Genís, A., Giraldo, B. F., (2017). Assessment of respiratory flow cycle morphology in patients with chronic heart failure Medical and Biological Engineering and Computing 55, (2), 245-255

Breathing pattern as periodic breathing (PB) in chronic heart failure (CHF) is associated with poor prognosis and high mortality risk. This work investigates the significance of a number of time domain parameters for characterizing respiratory flow cycle morphology in patients with CHF. Thus, our primary goal is to detect PB pattern and identify patients at higher risk. In addition, differences in respiratory flow cycle morphology between CHF patients (with and without PB) and healthy subjects are studied. Differences between these parameters are assessed by investigating the following three classification issues: CHF patients with PB versus with non-periodic breathing (nPB), CHF patients (both PB and nPB) versus healthy subjects, and nPB patients versus healthy subjects. Twenty-six CHF patients (8/18 with PB/nPB) and 35 healthy subjects are studied. The results show that the maximal expiratory flow interval is shorter and with lower dispersion in CHF patients than in healthy subjects. The flow slopes are much steeper in CHF patients, especially for PB. Both inspiration and expiration durations are reduced in CHF patients, mostly for PB. Using the classification and regression tree technique, the most discriminant parameters are selected. For signals shorter than 1 min, the time domain parameters produce better results than the spectral parameters, with accuracies for each classification of 82/78, 89/85, and 91/89 %, respectively. It is concluded that morphologic analysis in the time domain is useful, especially when short signals are analyzed.

Keywords: Chronic heart failure, Ensemble average, Periodic and non-periodic breathing, Respiratory pattern


Lozano, M., Fiz, J. A., Jané, R., (2016). Automatic differentiation of normal and continuous adventitious respiratory sounds using ensemble empirical mode decomposition and instantaneous frequency IEEE Journal of Biomedical and Health Informatics 20, (2), 486-497

Differentiating normal from adventitious respiratory sounds (RS) is a major challenge in the diagnosis of pulmonary diseases. Particularly, continuous adventitious sounds (CAS) are of clinical interest because they reflect the severity of certain diseases. This study presents a new classifier that automatically distinguishes normal sounds from CAS. It is based on the multi-scale analysis of instantaneous frequency (IF) and envelope (IE) calculated after ensemble empirical mode decomposition (EEMD). These techniques have two major advantages over previous techniques: high temporal resolution is achieved by calculating IF-IE and a priori knowledge of signal characteristics is not required for EEMD. The classifier is based on the fact that the IF dispersion of RS signals markedly decreases when CAS appear in respiratory cycles. Therefore, CAS were detected by using a moving window to calculate the dispersion of IF sequences. The study dataset contained 1494 RS segments extracted from 870 inspiratory cycles recorded from 30 patients with asthma. All cycles and their RS segments were previously classified as containing normal sounds or CAS by a highly experienced physician to obtain a gold standard classification. A support vector machine classifier was trained and tested using an iterative procedure in which the dataset was randomly divided into training (65%) and testing (35%) sets inside a loop. The SVM classifier was also tested on 4592 simulated CAS cycles. High total accuracy was obtained with both recorded (94.6% ± 0.3%) and simulated (92.8% ± 3.6%) signals. We conclude that the proposed method is promising for RS analysis and classification.

Keywords: Diseases, Dispersion, Empirical mode decomposition, Feature extraction, Informatics, Support vector machines


Estrada, L., Torres, A., Sarlabous, L., Jané, R., (2016). Improvement in neural respiratory drive estimation from diaphragm electromyographic signals using fixed sample entropy IEEE Journal of Biomedical and Health Informatics 20, (2), 476-485

Diaphragm electromyography is a valuable technique for the recording of electrical activity of the diaphragm. The analysis of diaphragm electromyographic (EMGdi) signal amplitude is an alternative approach for the quantification of neural respiratory drive (NRD). The EMGdi signal is, however, corrupted by electrocardiographic (ECG) activity, and this presence of cardiac activity can make the EMGdi interpretation more difficult. Traditionally, the EMGdi amplitude has been estimated using the average rectified value (ARV) and the root mean square (RMS). In this work, surface EMGdi signals were analyzed using the fixed sample entropy (fSampEn) algorithm, and compared to traditional ARV and RMS methods. The fSampEn is calculated using a tolerance value fixed and independent of the standard deviation of the analysis window. Thus, this method quantifies the amplitude of the complex components of stochastic signals (such as EMGdi), and being less affected by changes in amplitude due to less complex components (such as ECG). The proposed method was tested in synthetic and recorded EMGdi signals. fSampEn was less sensitive to the effect of cardiac activity on EMGdi signals with different levels of NRD than ARV and RMS amplitude parameters. The mean and standard deviation of the Pearson’s correlation values between inspiratory mouth pressure (an indirect measure of the respiratory muscle activity) and fSampEn, ARV and RMS parameters, estimated in the recorded EMGdi signal at tidal volume (without inspiratory load), were 0.38???0.12, 0.27???0.11 and 0.11???0.13, respectively. Whereas at 33 cmH2O (maximum inspiratory load) were 0.83???0.02, 0.76???0.07 and 0.61???0.19, respectively. Our findings suggest that the proposed method may improve the evaluation of NRD.

Keywords: Electromyography, diaphragm muscle, neural respiratory drive


Lozano, Manuel, Fiz, J. A., Jané, Raimon, (2016). Performance evaluation of the Hilbert–Huang transform for respiratory sound analysis and its application to continuous adventitious sound characterization Signal Processing 120, 99-116

Abstract The use of the Hilbert–Huang transform in the analysis of biomedical signals has increased during the past few years, but its use for respiratory sound (RS) analysis is still limited. The technique includes two steps: empirical mode decomposition (EMD) and instantaneous frequency (IF) estimation. Although the mode mixing (MM) problem of EMD has been widely discussed, this technique continues to be used in many RS analysis algorithms. In this study, we analyzed the MM effect in RS signals recorded from 30 asthmatic patients, and studied the performance of ensemble EMD (EEMD) and noise-assisted multivariate EMD (NA-MEMD) as means for preventing this effect. We propose quantitative parameters for measuring the size, reduction of MM, and residual noise level of each method. These parameters showed that EEMD is a good solution for MM, thus outperforming NA-MEMD. After testing different IF estimators, we propose Kay׳s method to calculate an EEMD-Kay-based Hilbert spectrum that offers high energy concentrations and high time and high frequency resolutions. We also propose an algorithm for the automatic characterization of continuous adventitious sounds (CAS). The tests performed showed that the proposed EEMD-Kay-based Hilbert spectrum makes it possible to determine CAS more precisely than other conventional time-frequency techniques.

Keywords: Hilbert–Huang transform, Ensemble empirical mode decomposition, Instantaneous frequency, Respiratory sounds, Continuous adventitious sounds


Ramón Valencia, J. L., García-Sánchez, A., Roca-Dorda, J., Giraldo, B. F., (2016). Análisis de la señal ECG en pacientes con enfermedad de Párkinson CASEIB Proceedings XXXIV Congreso Anual de la Sociedad Española de Ingeniería Biomédica (CASEIB 2016) , Sociedad Española de Ingeniería Biomédica (Valencia, Spain) , 552-555

La enfermedad del Párkinson es un tipo de trastorno del movimiento, causado por la degeneración de las células dopaminérgicas. La variabilidad del ritmo cardíaco en estos pacientes se puede ver alterada como consecuencia de la actividad motora. El estudio de esta variabilidad puede ayudar en el diagnóstico y análisis de la evolución de la enfermedad en estos pacientes. En este estudio se propone el análisis de parámetros extraídos de la señal electrocardiográfica en pacientes enfermos de Párkinson, con el propósito de obtener índices que puedan ser indicadores de esta enfermedad. Se propone un protocolo para registrar la señal ECG considerando 4 actividades diferentes. Se registraron 19 pacientes y 16 sujetos sanos. Las señales fueron analizadas en el dominio temporal considerando los intervalos RR de la señal ECG, y en el dominio frecuencial, considerando las bandas de muy baja (VLF: 0-0.05 Hz), baja (LF: 0.05-0.15 Hz) y alta (HF: 0.15-0.4 Hz) frecuencia, respectivamente. De acuerdo con los resultados obtenidos, el índice de la actividad simpática presentó diferencias estadísticamente significativas al comparar pacientes versus sanos, durante las 4 actividades desarrolladas. El intervalo RR también es un indicador de la variación de la actividad cardíaca en los pacientes, especialmente al compararlos en el estado basal. Índices que relacionen parámetros temporales y frecuenciales podrían ser un claro indicador de la actividad cardiovascular de los pacientes enfermos de Párkinson.


Arcentales, A., Rivera, P., Caminal, P., Voss, A., Bayés-Genís, A., Giraldo, B. F., (2016). Analysis of blood pressure signal in patients with different ventricular ejection fraction using linear and non-linear methods Engineering in Medicine and Biology Society (EMBC) 38th Annual International Conference of the IEEE , IEEE (Orlando, USA) , 2700-2703

Changes in the left ventricle function produce alternans in the hemodynamic and electric behavior of the cardiovascular system. A total of 49 cardiomyopathy patients have been studied based on the blood pressure signal (BP), and were classified according to the left ventricular ejection fraction (LVEF) in low risk (LR: LVEF>35%, 17 patients) and high risk (HR: LVEF≤35, 32 patients) groups. We propose to characterize these patients using a linear and a nonlinear methods, based on the spectral estimation and the recurrence plot, respectively. From BP signal, we extracted each systolic time interval (STI), upward systolic slope (BPsl), and the difference between systolic and diastolic BP, defined as pulse pressure (PP). After, the best subset of parameters were obtained through the sequential feature selection (SFS) method. According to the results, the best classification was obtained using a combination of linear and nonlinear features from STI and PP parameters. For STI, the best combination was obtained considering the frequency peak and the diagonal structures of RP, with an area under the curve (AUC) of 79%. The same results were obtained when comparing PP values. Consequently, the use of combined linear and nonlinear parameters could improve the risk stratification of cardiomyopathy patients.

Keywords: Feature extraction, Blood pressure, Heart rate, Estimation, Data mining, Covariance matrices, Hospitals


Rodriguez, J., Voss, A., Caminal, P., Bayés-Genís, A., Giraldo, B. F., (2016). Caracterización de pacientes con diferentes niveles de riesgo cardiovascular mediante diagramas de Poincaré CASEIB Proceedings XXXIV Congreso Anual de la Sociedad Española de Ingeniería Biomédica (CASEIB 2016) , Sociedad Española de Ingeniería Biomédica (Valencia, Spain) , 396-399

En este trabajo se propone caracterizar la dinámica no-lineal de los sistemas cardíaco, vascular y respiratorio a partir de los diagramas de Poincaré. Se han analizado 46 pacientes con cardiomiopatía isquémica (ICM) o dilatada (DCM), y 35 sujetos sanos. De acuerdo con su fracción de eyección ventricular izquierda (LVEF), los pacientes también fueron clasificados en un grupo de alto riesgo (HR: LVEF ≤ 35%, 30 pacientes) y otro de bajo riesgo (LR: LVEF > 35%, 16 pacientes). A partir de las señales electrocardiográfica, de flujo respiratorio y de presión sanguínea se han obtenido los datos relacionados con el tiempo entre latidos cardíacos (RR), entre valores máximos de presión sistólica (SBP), y la duración del ciclo respiratorio (TTot). Estas series temporales han sido representadas mediante los diagramas de Poincaré, y caracterizadas teniendo en cuenta su desviación a largo plazo (SD1) y su cambio instantáneo (SD2). De acuerdo con los resultados obtenidos, los parámetros de las series cardíaca y de presión sanguínea, relacionados con las diagonales longitudinales y transversales del diagrama de Poincaré, son los que mejor diferencian entre pacientes con HR vs LR. Para la clasificación de pacientes isquémicos vs dilatados, los mejores parámetros se obtuvieron a partir de las series respiratorias y están relacionados con las distancias de la desviación estándar a la línea de identidad. Los cambios en estas relaciones representan una mayor aceleración en la dinámica respiratoria de los pacientes con cardiomiopatía isquémica.


Ràfols-de-Urquía, M., Estévez-Piorno, J., Torres, A., Estrada, L., Jané, R., (2016). Evaluación de un dispositivo inalámbrico para el registro de la actividad electromiográfica del músculo diafragma CASEIB Proceedings XXXIV Congreso Anual de la Sociedad Española de Ingeniería Biomédica (CASEIB 2016) , Sociedad Española de Ingeniería Biomédica (Valencia, Spain) , 244-247

La evaluación clínica y deportiva requiere el desarrollo de sistemas de adquisición de señales biomédicas de alta calidad. Sin embargo, estos sistemas implican una gran limitación: los datos deben ser registrados en laboratorios. En los últimos años se han desarrollado dispositivos inalámbricos multimodales que pueden poner fin a estos problemas. En este proyecto se han evaluado señales electromiográficas de los músculos respiratorios, en especial del diafragma (EMGdi), obtenidas a partir de un dispositivo inalámbrico. De forma simultánea se han adquirido las mismas señales con un sistema estándar de adquisición de señales biomédicas, para realizar un estudio comparativo de parámetros e información fisiológica extraída de dichas señales. Las señales han sido registradas en 9 sujetos sanos que siguieron un protocolo respiratorio. Estas señales han sido filtradas y procesadas usando técnicas basadas en el dominio frecuencial y temporal. El ritmo cardíaco ha sido estimado tanto a partir de la medida directa del registro ECG, como indirectamente a partir de las señales electromiografícas respiratorias, mientras que el ritmo respiratorio y la fuerza del músculo han sido estimados a partir de la amplitud de las señales EMGdi durante la contracción respiratoria. Los resultados obtenidos de los datos registrados por sistemas inalámbricos son muy similares a los obtenidos mediante sistemas convencionales con cables, demostrando ser una alternativa que permite adquisiciones y estudios fuera de los laboratorios en situaciones mucho más reales.


Estévez-Piorno, J., Ràfols-de-Urquía, M., Torres, A., Estrada, L., Jané, R., (2016). Evaluación del registro y transmisión de señales electromiográficas mediante un dispostivo inalámbrico CASEIB Proceedings XXXIV Congreso Anual de la Sociedad Española de Ingeniería Biomédica (CASEIB 2016) , Sociedad Española de Ingeniería Biomédica (Valencia, Spain) , 556-559

Obstructive sleep apnea (OSA) is a highly prevalent chronic disease, especially in elderly and obese population. Despite constituting a huge health and economic problem, most patients remain undiagnosed due to limitations in current strategies. Therefore, it is essential to find cost-effective diagnostic alternatives. One of these novel approaches is the analysis of acoustic snoring signals. Snoring is an early symptom of OSA which carries pathophysiological information of high diagnostic value. For this reason, the main objective of this work is to study the characteristics of single snores of different types, from healthy and OSA subjects. To do that, we analyzed snoring signals from previous databases and developed an experimental protocol to record simulated OSA-related sounds and characterize the response of two commercial tracheal microphones. Automatic programs for filtering, downsampling, event detection and time-frequency analysis were built in MATLAB. We found that time-frequency maps and spectral parameters (central, mean and peak frequency and energy in the 100-500 Hz band) allow distinguishing regular snores of healthy subjects from non-regular snores and snores of OSA subjects. Regarding the two commercial microphones, we found that one of them was a suitable snoring sensor, while the other had a too restricted frequency response. Future work shall include a higher number of episodes and subjects, but our study has contributed to show how important the differences between regular and non-regular snores can be for OSA diagnosis, and how much clinically relevant information can be extracted from time-frequency maps and spectral parameters of single snores.


Estrada, L., Torres, A., Sarlabous, L., Jané, R., (2016). Evaluating respiratory muscle activity using a wireless sensor platform Engineering in Medicine and Biology Society (EMBC) 38th Annual International Conference of the IEEE , IEEE (Orlando, USA) , 5769-5772

Wireless sensors are an emerging technology that allows to assist physicians in the monitoring of patients health status. This approach can be used for the non-invasive recording of the electrical respiratory muscle activity of the diaphragm (EMGdi). In this work, we acquired the EMGdi signal of a healthy subject performing an inspiratory load test. To this end, the EMGdi activity was captured from a single channel of electromyography using a wireless platform which was compared with the EMGdi and the inspiratory mouth pressure (Pmouth) recorded with a conventional lab equipment. From the EMGdi signal we were able to evaluate the neural respiratory drive, a biomarker used for assessing the respiratory muscle function. In addition, we evaluated the breathing movement and the cardiac activity, estimating two cardio-respiratory parameters: the respiratory rate and the heart rate. The correlation between the two EMGdi signals and the Pmouth improved with increasing the respiratory load (Pearson's correlation coefficient ranges from 0.33 to 0.85). The neural respiratory drive estimated from both EMGdi signals showed a positive trend with an increase of the inspiratory load and being higher in the conventional EMGdi recording. The respiratory rate comparison between measurements revealed similar values of around 16 breaths per minute. The heart rate comparison showed a root mean error of less than 0.2 beats per minute which increased when incrementing the inspiratory load. In summary, this preliminary work explores the use of wireless devices to record the muscle respiratory activity to derive several physiological parameters. Its use can be an alternative to conventional measuring systems with the advantage of being portable, lightweight, flexible and operating at low energy. This technology can be attractive for medical staff and may have a positive impact in the way healthcare is being delivered.

Keywords: Biomedical monitoring, Electrodes, Medical services, Monitoring, Muscles, Wireless communication, Wireless sensor networks


Argerich, S., Herrera, S., Benito, S., Giraldo, J., (2016). Evaluation of periodic breathing in respiratory flow signal of elderly patients using SVM and linear discriminant analysis Engineering in Medicine and Biology Society (EMBC) 38th Annual International Conference of the IEEE , IEEE (Orlando, USA) , 4276-4279

Aging population is a major concern that is reflected in the increase of chronic diseases. Heart Failure (HF) is one of the most common chronic diseases of elderly people that is punctuated with acute episodes, which result in hospitalization. The periodic modulation of the amplitude of the breathing pattern is proved to be one of the multiple symptoms of an acute episode, and thus, the features extracted from its characterization contribute in the improvement of the first diagnosis of the clinical practice. The main objective of this study is to evaluate if the features extracted from the breathing pattern along with common clinical variables are reliable enough to detect Periodic Breathing (PB). A dataset of 44 elderly patients containing clinical information and a short record of electrocardiogram and respiratory flow signal was used to train two machine learning classification methods: Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA). All the available clinical parameters within the dataset along with the parameters characterizing the respiratory pattern were used to classify the observations into two groups. SVM classification was optimized and performed using a = -8 and C = 10.04 giving an accuracy of 88.2 % sensitivity of 90 % and specificity of 85.7 % Similar results were achieved with LDA classifying with an accuracy of 82.4 %, a sensitivity of 81.8% and specificity of 83.3 % PB has been accurately detected using both classifiers.

Keywords: Support vector machines, Feature extraction, Training, Senior citizens, Standards, Training data, Hospitals


Julian, S., Callicó, F., Giraldo, B. F., Juanola, A., López, D., Rodiera, J., (2016). Segmentación del nodo vesical a partir del plano transversal de imágenes ecográficas de la región suprapúbica CASEIB Proceedings XXXIV Congreso Anual de la Sociedad Española de Ingeniería Biomédica (CASEIB 2016) , Sociedad Española de Ingeniería Biomédica (Valencia, Spain) , 278-281

La retención urinaria después de una cirugía anestésica puede provocar sobre-distensión vesical, impidiendo al paciente miccionar de forma voluntaria. La cateterización es el método más utilizado para solucionar este problema. El método es aplicado cuando el volumen vesical es superior a 300 ml. En este trabajo propone un método para la binarización y segmentación del nodo vesical a partir de una imagen ecográfica de la región suprapúbica transversal. Se han analizado 180 imágenes (80 de entrenamiento y 100 de validación), segmentadas utilizando el método de nivel de gris. Las imágenes fueron caracterizadas a partir de las líneas vertical, horizontal y las dos diagonales. Los valores obtenidos fueron comparados con los medidos con el ecógrafo, con un 72% de acierto. Se ha propuesto un método radial para el cierre de aperturas lateral e interior. Ajustados el brillo y la profundidad de la imagen, y el control morfológico se obtuvo hasta un 83% de correcta segmentación del área vesical en el grupo validación de la muestra. Estos resultados son la base para el cálculo del volumen de orina en vejiga y la decisión de cateterizar un paciente con retención urinaria.


Solà-Soler, J., Giraldo, B. F., Fiz, J. A., Jané, R., (2016). Study of phase estimation methods to analyse cardiorespiratory synchronization in OSA patients Engineering in Medicine and Biology Society (EMBC) 38th Annual International Conference of the IEEE , IEEE (Orlando, USA) , 4280-4283

Obstructive Sleep Apnea (OSA) is a sleep disorder highly prevalent in the general population. Cardiorespiratory Phase Synchronization (CRPS) is a form of non-linear interaction between respiratory and cardiovascular systems that was found to be reduced in severe OSA patients. The Hilbert Transform (HT) method was the recommended choice for estimating the respiratory phase in CRPS studies. But we have noticed that HT provides a phase that is aligned to the transition between the exhalation and the inhalation parts of different breathing cycles, instead of being aligned to the breathing onsets. In this work we proposed a Realigned HT phase estimation method (RHT) and we compared it to the conventional HT and to the Linear Phase (LP) approximation for estimating CRPS in a database of 28 patients with different OSA severity levels. RHT provided similar synchronization percentages (%Sync) as HT, and it enhanced the significant differences in %Sync between mild and severe OSA patients. %Sync showed the highest negative correlation with the Apnea-Hypopnea Index (AHI) when using RHT (rAHI=-0.692, p<;0.001), which only had an 10% extra computational cost. On the other hand, LP method significantly overestimated %Sync especially in the more severe patients, because it was unable to track the phase non-linearities that can be observed during sleep disordered breathing. Therefore, the newly proposed RHT can be the preferred alternative over the conventional HT or the LP approximation for estimating CRPS in OSA patients.

Keywords: Correlation, Databases, Electrocardiography, Phase estimation, Sleep apnea, Synchronization, Transforms


Castillo, Y., Blanco, D., Cámara, M.A., Jané, R., (2016). Study of time-frequency characteristics of single snores: extracting new information for sleep apnea diagnosis CASEIB Proceedings XXXIV Congreso Anual de la Sociedad Española de Ingeniería Biomédica (CASEIB 2016) , Sociedad Española de Ingeniería Biomédica (Valencia, Spain) , 105-108

Obstructive sleep apnea (OSA) is a highly prevalent chronic disease, especially in elderly and obese population. Despite constituting a huge health and economic problem, most patients remain undiagnosed due to limitations in current strategies. Therefore, it is essential to find cost-effective diagnostic alternatives. One of these novel approaches is the analysis of acoustic snoring signals. Snoring is an early symptom of OSA which carries pathophysiological information of high diagnostic value. For this reason, the main objective of this work is to study the characteristics of single snores of different types, from healthy and OSA subjects. To do that, we analyzed snoring signals from previous databases and developed an experimental protocol to record simulated OSA-related sounds and characterize the response of two commercial tracheal microphones. Automatic programs for filtering, downsampling, event detection and time-frequency analysis were built in MATLAB. We found that time-frequency maps and spectral parameters (central, mean and peak frequency and energy in the 100-500 Hz band) allow distinguishing regular snores of healthy subjects from non-regular snores and snores of OSA subjects. Regarding the two commercial microphones, we found that one of them was a suitable snoring sensor, while the other had a too restricted frequency response. Future work shall include a higher number of episodes and subjects, but our study has contributed to show how important the differences between regular and non-regular snores can be for OSA diagnosis, and how much clinically relevant information can be extracted from time-frequency maps and spectral parameters of single snores.


Estrada, L., Torres, A., Garcia-Casado, J., Sarlabous, L., Prats-Boluda, G., Jané, R., (2016). Time-frequency representations of the sternocleidomastoid muscle electromyographic signal recorded with concentric ring electrodes Engineering in Medicine and Biology Society (EMBC) 38th Annual International Conference of the IEEE , IEEE (Orlando, USA) , 3785-3788

The use of non-invasive methods for the study of respiratory muscle signals can provide clinical information for the evaluation of the respiratory muscle function. The aim of this study was to evaluate time-frequency characteristics of the electrical activity of the sternocleidomastoid muscle recorded superficially by means of concentric ring electrodes (CREs) in a bipolar configuration. The CREs enhance the spatial resolution, attenuate interferences, as the cardiac activity, and also simplify the orientation problem associated to the electrode location. Five healthy subjects underwent a respiratory load test in which an inspiratory load was imposed during the inspiratory phase. During the test, the electromyographic signal of the sternocleidomastoid muscle (EMGsc) and the inspiratory mouth pressure (Pmouth) were acquired. Time-frequency characteristics of the EMGsc signal were analyzed by means of eight time-frequency representations (TFRs): the spectrogram (SPEC), the Morlet scalogram (SCAL), the Wigner-Ville distribution (WVD), the Choi-Williams distribution (CHWD), two generalized exponential distributions (GED1 and GED2), the Born-Jordan distribution (BJD) and the Cone-Kernel distribution (CKD). The instantaneous central frequency of the EMGsc showed an increasing behavior during the inspiratory cycle and with the increase of the inspiratory load. The bilinear TFRs (WVD, CHWD, GEDs and BJD) were less sensitive to cardiac activity interference than classical TFRs (SPEC and SCAL). The GED2 was the TFR that shown the best results for the characterization of the instantaneous central frequency of the EMGsc.

Keywords: Electrodes, Interference, Kernel, Mouth, Muscles, Spectrogram, Time-frequency analysis


Sarlabous, Leonardo, Torres, Abel, Fiz, José A., Gea, Joaquim, Martínez-Llorens, Juana M., Jané, Raimon, (2015). Efficiency of mechanical activation of inspiratory muscles in COPD using sample entropy European Respiratory Journal 46, (6), 1808-1811

Respiratory muscle dysfunction is a common problem in patients with chronic obstructive pulmonary disease (COPD) and has mostly been related to pulmonary hyperinflation [1, 2]. Associated diaphragm shortening and deleterious changes in the muscle force-length relationship cause a reduction in the muscles' capacity to generate pressure, placing them at a mechanical disadvantage [1, 3]. Specifically, both inspiratory muscle strength and mechanical efficiency may be reduced in COPD patients [1, 4–6], although, at isovolume, the contractile strength of the diaphragm in COPD is preserved or may even be improved in some cases [7]. The ratio between transdiaphragmatic pressure and electrical diaphragm activity has been used as a measure of respiratory muscle efficiency [8, 9]. However, in clinical practice, it is complex to measure this parameter directly, as invasive measures are required and these are uncomfortable for patients [4].


Garde, A., Giraldo, B. F., Jané, R., Latshang, T. D., Turk, A. J., Hess, T., Bosch, M-.M., Barthelmes, D., Merz, T. M., Hefti, J. Pichler, Schoch, O. D., Bloch, K. E., (2015). Time-varying signal analysis to detect high-altitude periodic breathing in climbers ascending to extreme altitude Medical & Biological Engineering & Computing 53, (8), 699-712

This work investigates the performance of cardiorespiratory analysis detecting periodic breathing (PB) in chest wall recordings in mountaineers climbing to extreme altitude. The breathing patterns of 34 mountaineers were monitored unobtrusively by inductance plethysmography, ECG and pulse oximetry using a portable recorder during climbs at altitudes between 4497 and 7546 m on Mt. Muztagh Ata. The minute ventilation (VE) and heart rate (HR) signals were studied, to identify visually scored PB, applying time-varying spectral, coherence and entropy analysis. In 411 climbing periods, 30–120 min in duration, high values of mean power (MPVE) and slope (MSlopeVE) of the modulation frequency band of VE, accurately identified PB, with an area under the ROC curve of 88 and 89 %, respectively. Prolonged stay at altitude was associated with an increase in PB. During PB episodes, higher peak power of ventilatory (MPVE) and cardiac (MP LF HR ) oscillations and cardiorespiratory coherence (MP LF Coher ), but reduced ventilation entropy (SampEnVE), was observed. Therefore, the characterization of cardiorespiratory dynamics by the analysis of VE and HR signals accurately identifies PB and effects of altitude acclimatization, providing promising tools for investigating physiologic effects of environmental exposures and diseases.

Keywords: High-altitude periodic breathing, Cardiorespiratory characterization, Time-varying spectral analysis, Acclimatization, Hypoxia


Arcentales, A., Caminal, P., Diaz, I., Benito, S., Giraldo, B., (2015). Classification of patients undergoing weaning from mechanical ventilation using the coherence between heart rate variability and respiratory flow signal Physiological Measurement 36, (7), 1439-1452

Weaning from mechanical ventilation is still one of the most challenging problems in intensive care. Unnecessary delays in discontinuation and weaning trials that are undertaken too early are both undesirable. This study investigated the contribution of spectral signals of heart rate variability (HRV) and respiratory flow, and their coherence to classifying patients on weaning process from mechanical ventilation. A total of 121 candidates for weaning, undergoing spontaneous breathing tests, were analyzed: 73 were successfully weaned (GSucc), 33 failed to maintain spontaneous breathing so were reconnected (GFail), and 15 were extubated after the test but reintubated within 48 h (GRein). The power spectral density and magnitude squared coherence (MSC) of HRV and respiratory flow signals were estimated. Dimensionality reduction was performed using principal component analysis (PCA) and sequential floating feature selection. The patients were classified using a fuzzy K-nearest neighbour method. PCA of the MSC gave the best classification with the highest accuracy of 92% classifying GSucc versus GFail patients, and 86% classifying GSucc versus GRein patients. PCA of the respiratory flow signal gave the best classification between GFail and GRein patients (79% accuracy). These classifiers showed a good balance between sensitivity and specificity. Besides, the spectral coherence between HRV and the respiratory flow signal, in patients on weaning trial process, can contribute to the extubation decision.


Lozano, M., Fiz, J. A., Jané, R., (2015). Análisis de sonidos adventicios continuos en pacientes asmáticos mediante el espectro de Hilbert CASEIB Proceedings XXXIII Congreso Anual de la Sociedad Española de Ingeniería Biomédica (CASEIB 2015) , Sociedad Española de Ingeniería Biomédica (Madrid, Spain) , 179-182

Los sonidos adventicios continuos (CAS) son uno de los principales síntomas del asma. Dada su importancia clínica, el análisis de estas señales requiere del uso de técnicas que permitan segmentarlas y caracterizarlas con una precisión alta. Sin embargo, la mayoría de técnicas propuestas anteriormente estaban basadas en el análisis de Fourier o wavelet, técnicas que tienen una resolución limitada a priori y son altamente dependientes de la amplitud de los CAS. En este estudio se presenta una técnica alternativa para el análisis de CAS basada en el espectro de Hilbert. El método presentado combina la descomposición empírica en modos por conjuntos con el estimador de Kay de la frecuencia instantánea, para obtener una representación tiempo-frecuencia con una alta concentración de energía y una resolución temporal y frecuencial elevada. Con el fin de mostrar las ventajas que ofrece el método presentado, se ha aplicado a cuatro señales de sonidos respiratorios registradas en pacientes asmáticos que contienen distintos tipos de CAS, reforzando la hipótesis confirmada en nuestro estudio previo de que el espectro de Hilbert permite segmentar y caracterizar los CAS con mayor precisión que otras técnicas tradicionales ampliamente utilizadas, como el espectrograma.


Giraldo, B. F., Rodríguez, J., Arcentales, A., Voss, A., Caminal, P., Bayes-Genis, A., (2015). Caracterización de pacientes isquémicos y dilatados a partir de las señales ECG y de presión sanguínea CASEIB Proceedings XXXIII Congreso Anual de la Sociedad Española de Ingeniería Biomédica (CASEIB 2015) , Sociedad Española de Ingeniería Biomédica (Madrid, Spain) , 31-34

Las enfermedades cardiovasculares son una de las principales causas de muerte en países desarrollados. Se han analizado 42 pacientes con cardiomiopatía isquémica (ICM) o dilatada (DCM), clasificados en función de la fracción de eyección ventricular izquierda (LVEF), en grupos de alto riesgo (HR: LVEF


Giraldo, B. F., Rodriguez, J., Caminal, P., Bayes-Genis, A., Voss, A., (2015). Cardiorespiratory and cardiovascular interactions in cardiomyopathy patients using joint symbolic dynamic analysis Engineering in Medicine and Biology Society (EMBC) 37th Annual International Conference of the IEEE , IEEE (Milan, Italy) , 306-309

Cardiovascular diseases are the first cause of death in developed countries. Using electrocardiographic (ECG), blood pressure (BP) and respiratory flow signals, we obtained parameters for classifying cardiomyophaty patients. 42 patients with ischemic (ICM) and dilated (DCM) cardiomyophaties were studied. The left ventricular ejection fraction (LVEF) was used to stratify patients with low risk (LR: LVEF>35%, 14 patients) and high risk (HR: LVEF≤ 35%, 28 patients) of heart attack. RR, SBP and TTot time series were extracted from the ECG, BP and respiratory flow signals, respectively. The time series were transformed to a binary space and then analyzed using Joint Symbolic Dynamic with a word length of three, characterizing them by the probability of occurrence of the words. Extracted parameters were then reduced using correlation and statistical analysis. Principal component analysis and support vector machines methods were applied to characterize the cardiorespiratory and cardiovascular interactions in ICM and DCM cardiomyopaties, obtaining an accuracy of 85.7%.

Keywords: Blood pressure, Electrocardiography, Joints, Kernel, Principal component analysis, Support vector machines, Time series analysis


Sola-Soler, J., Giraldo, B. F., Fiz, J. A., Jané, R., (2015). Cardiorespiratory Phase Synchronization in OSA subjects during wake and sleep states Engineering in Medicine and Biology Society (EMBC) 37th Annual International Conference of the IEEE , IEEE (Milan, Italy) , 7708-7711

Cardiorespiratory Phase Synchronization (CRPS) is a manifestation of coupling between cardiac and respiratory systems complementary to Respiratory Sinus Arrhythmia. In this work, we investigated CRPS during wake and sleep stages in Polysomnographic (PSG) recordings of 30 subjects suspected from Obstructive Sleep Apnea (OSA). The population was classified into three severity groups according to the Apnea Hypopnea Index (AHI): G1 (AHI<;15), G2 (15<;=AHI<;30) and G3 (AHI>30). The synchrogram between single lead ECG and respiratory abdominal band signals from PSG was computed with the Hilbert transform technique. The different phase locking ratios (PLR) m:n were monitored throughout the night. Ratio 4:1 was the most frequent and it became more dominant as OSA severity increased. CRPS was characterized by the percentage of synchronized time (%Sync) and the average duration of synchronized epochs (AvDurSync) using three different thresholds. Globally, we observed that %Sync significantly decreased and AvDurSync slightly increased with OSA severity. A high synchronization threshold enhanced these population differences. %Sync was significantly higher in NREM than in REM sleep in G2 and G3 groups. Population differences observed during sleep did not translate to the initial wake state. Reduced CRPS could be an early marker of OSA severity during sleep, but further studies are needed to determine whether CRPS is also present during wakefulness.

Keywords: Band-pass filters, Electrocardiography, Heart beat, Sleep apnea, Sociology, Statistics, Synchronization


Estrada, L., Torres, A., Sarlabous, L., Jané, R., (2015). EMG-derived respiration signal using the fixed sample entropy during an Inspiratory load protocol Engineering in Medicine and Biology Society (EMBC) 37th Annual International Conference of the IEEE , IEEE (Milan, Italy) , 1703-1706

Extracting clinical information from one single measurement represents a step forward in the assessment of the respiratory muscle function. This attracting idea entails the reduction of the instrumentation and fosters to develop new medical integrated technologies. We present the use of the fixed sample entropy (fSampEn) as a more direct method to non-invasively derive the breathing activity from the diaphragm electromyographic (EMGdi) signal, and thus to extract the respiratory rate, an important vital sign which is cumbersome and time-consuming to be measured by clinicians. fSampEn is a method to evaluate the EMGdi activity that is less sensitive to the cardiac activity (ECG) and its application has proven to be useful to evaluate the load of the respiratory muscles. The behavior of the proposed method was tested in signals from two subjects that performed an inspiratory load protocol, which consists of increments in the inspiratory mouth pressure (Pmouth). Two respiratory signals were derived and compared to the Pmouth signal: the ECG-derived respiration (EDR) signal from the lead-I configuration, and the EMG-derived respiration (EMGDR) signal by applying the fSampEn method over the EMGdi signal. The similitude and the lag between signals were calculated through the cross-correlation between each derived respiratory signal and the Pmouth. The EMGDR signal showed higher correlation and lower lag values (≥ 0.91 and ≤ 0.70 s, respectively) than the EDR signal (≥ 0.83 and ≤0.99 s, respectively). Additionally, the respiratory rate was estimated with the Pmouth, EDR and EMGDR signals showing very similar values. The results from this preliminary work suggest that the fSampEn method can be used to derive the respiration waveform from the respiratory muscle electrical activity.

Keywords: Band-pass filters, Electrocardiography, Electromyography, Entropy, Mouth, Muscles, Protocols


Estrada, L., Torres, A., Garcia-Casado, J., Sarlabous, L., Prats-Boluda, G., Jané, R., (2015). Evaluation of sternocleidomastoid muscle activity by electromyography recorded with concentric ring electrodes CASEIB Proceedings XXXIII Congreso Anual de la Sociedad Española de Ingeniería Biomédica (CASEIB 2015) , Sociedad Española de Ingeniería Biomédica (Madrid, Spain) , 183-186

Los sonidos adventicios continuos (CAS) son uno de los principales síntomas del asma. Dada su importancia clínica, el análisis de estas señales requiere del uso de técnicas que permitan segmentarlas y caracterizarlas con una precisión alta. Sin embargo, la mayoría de técnicas propuestas anteriormente estaban basadas en el análisis de Fourier o wavelet, técnicas que tienen una resolución limitada a priori y son altamente dependientes de la amplitud de los CAS. En este estudio se presenta una técnica alternativa para el análisis de CAS basada en el espectro de Hilbert. El método presentado combina la descomposición empírica en modos por conjuntos con el estimador de Kay de la frecuencia instantánea, para obtener una representación tiempo-frecuencia con una alta concentración de energía y una resolución temporal y frecuencial elevada. Con el fin de mostrar las ventajas que ofrece el método presentado, se ha aplicado a cuatro señales de sonidos respiratorios registradas en pacientes asmáticos que contienen distintos tipos de CAS, reforzando la hipótesis confirmada en nuestro estudio previo de que el espectro de Hilbert permite segmentar y caracterizar los CAS con mayor precisión que otras técnicas tradicionales ampliamente utilizadas, como el espectrograma.


Urra, O., Casals, A., Jané, R., (2015). The impact of visual feedback on the motor control of the upper-limb Engineering in Medicine and Biology Society (EMBC) 37th Annual International Conference of the IEEE , IEEE (Milan, Italy) , 3945-3948

Stroke is a leading cause of adult disability with upper-limb hemiparesis being one of the most frequent consequences. Given that stroke only affects the paretic arm's control structure (the set of synergies and activation vectors needed to perform a movement), we propose that the control structure of the non-affected arm can serve as a physiological reference to rehabilitate the paretic arm. However, it is unclear how rehabilitation can effectively tune the control structure of a patient. The use of Visual Feedback (VF) is recommended to boost stroke rehabilitation, as it is able to positively modify neural mechanisms and improve motor performance. Thus, in this study we investigate whether VF can effectively modify the control structure of the upper-limb. We asked six neurologically intact subjects to perform a complete upper-limb rehabilitation routine comprised of 12 movements in absence and presence of VF. Our results indicate that VF significantly increases interlimb similarity both in terms of synergies and activation coefficients. However, the magnitude of improvement depended upon each subject. In general, VF brings the control structure of the nondominant side closer to the control structure of dominant side, suggesting that VF modifies the control structure towards more optimized motor patterns. This is especially interesting because stroke mainly affects the activation coefficients of patients and because it has been shown that the control of the affected side resembles that of the nondominant side. In conclusion, VF may enhance motor performance by effectively tuning the control-structure. Notably, this finding offers new insights to design improved stroke rehabilitation.

Keywords: Bars, Biomedical engineering, Electrodes, Electromyography, Mirrors, Muscles, Visualization


Estrada, L., Torres, A., Sarlabous, L., Jané, R., (2015). Respiratory signal derived from the smartphone built-in accelerometer during a Respiratory Load Protocol Engineering in Medicine and Biology Society (EMBC) 37th Annual International Conference of the IEEE , IEEE (Milan, Italy) , 6768-6771

The scope of our work focuses on investigating the potential use of the built-in accelerometer of the smartphones for the recording of the respiratory activity and deriving the respiratory rate. Five healthy subjects performed an inspiratory load protocol. The excursion of the right chest was recorded using the built-in triaxial accelerometer of a smartphone along the x, y and z axes and with an external uniaxial accelerometer. Simultaneously, the respiratory airflow and the inspiratory mouth pressure were recorded, as reference respiratory signals. The chest acceleration signal recorded in the z axis with the smartphone was denoised using a scheme based on the ensemble empirical mode decomposition, a noise data assisted method which decomposes nonstationary and nonlinear signals into intrinsic mode functions. To distinguish noisy oscillatory modes from the relevant modes we use the detrended fluctuation analysis. We reported a very strong correlation between the acceleration of the z axis of the smartphone and the reference accelerometer across the inspiratory load protocol (from 0.80 to 0.97). Furthermore, the evaluation of the respiratory rate showed a very strong correlation (0.98). A good agreement was observed between the respiratory rate estimated with the chest acceleration signal from the z axis of the smartphone and with the respiratory airflow signal: Bland-Altman limits of agreement between -1.44 and 1.46 breaths per minute with a mean bias of -0.01 breaths per minute. This preliminary study provides a valuable insight into the use of the smartphone and its built-in accelerometer for respiratory monitoring.

Keywords: Acceleration, Accelerometers, Correlation, Empirical mode decomposition, Fluctuations, Protocols, Time series analysis


Urra, O., Casals, A., Jané, R., (2015). Visual feedback facilitates intermanual transfer of the motor control of the dominant arm towards the nondominant arm CASEIB Proceedings XXXIII Congreso Anual de la Sociedad Española de Ingeniería Biomédica (CASEIB 2015) , Sociedad Española de Ingeniería Biomédica (Madrid, Spain) , 503-506

Visual feedback (VF) is applied to recover motor skills after stroke. However, the exact mechanisms underlying the beneficial effects of VF remain unclear, limiting its optimal use in clinical practice. We previously reported that the effect of neural mechanisms triggered by VF is reflected in the upperlimb at the level of the control structure (the set of synergies/corresponding activation coefficients used to perform a task). Here, we hypothesize that VF may facilitate the transfer of superior motor programs stored in the dominant hemisphere to optimize the nondominant arm’s motor control. In order to test this hypothesis we have quantified the intermanual transfer (IMT) of the dominant control structure to the nondominant arm during the execution of a complete set of standard rehabilitation routines. We demonstrate that IMT is the main mechanism by which VF increases interlimb similarity and we show that the magnitude of IMT can be up to 75% in neurologically intact subjects. Thus, this study provides sound physiological evidence to encourage the use of VF in stroke rehabilitation.


Fiz, J. A., Jané, R., Lozano, M., Gómez, R., Ruiz, J., (2014). Detecting unilateral phrenic paralysis by acoustic respiratory analysis PLoS ONE 9, (4), e93595

The consequences of phrenic nerve paralysis vary from a considerable reduction in respiratory function to an apparently normal state. Acoustic analysis of lung sound intensity (LSI) could be an indirect non-invasive measurement of respiratory muscle function, comparing activity on the two sides of the thoracic cage. Lung sounds and airflow were recorded in ten males with unilateral phrenic paralysis and ten healthy subjects (5 men/5 women), during progressive increasing airflow maneuvers. Subjects were in sitting position and two acoustic sensors were placed on their back, on the left and right sides. LSI was determined from 1.2 to 2.4 L/s between 70 and 2000 Hz. LSI was significantly greater on the normal (19.3±4.0 dB) than the affected (5.7±3.5 dB) side in all patients (p = 0.0002), differences ranging from 9.9 to 21.3 dB (13.5±3.5 dB). In the healthy subjects, the LSI was similar on both left (15.1±6.3 dB) and right (17.4±5.7 dB) sides (p = 0.2730), differences ranging from 0.4 to 4.6 dB (2.3±1.6 dB). There was a positive linear relationship between the LSI and the airflow, with clear differences between the slope of patients (about 5 dB/L/s) and healthy subjects (about 10 dB/L/s). Furthermore, the LSI from the affected side of patients was close to the background noise level, at low airflows. As the airflow increases, the LSI from the affected side did also increase, but never reached the levels seen in healthy subjects. Moreover, the difference in LSI between healthy and paralyzed sides was higher in patients with lower FEV1 (%). The acoustic analysis of LSI is a relevant non-invasive technique to assess respiratory function. This method could reinforce the reliability of the diagnosis of unilateral phrenic paralysis, as well as the monitoring of these patients.


Sarlabous, Leonardo, Torres, Abel, Fiz, J. A., Jané, Raimon, (2014). Evidence towards improved estimation of respiratory muscle effort from diaphragm mechanomyographic signals with cardiac vibration interference using sample entropy with fixed tolerance values PLoS ONE 9, (2), e88902

The analysis of amplitude parameters of the diaphragm mechanomyographic (MMGdi) signal is a non-invasive technique to assess respiratory muscle effort and to detect and quantify the severity of respiratory muscle weakness. The amplitude of the MMGdi signal is usually evaluated using the average rectified value or the root mean square of the signal. However, these estimations are greatly affected by the presence of cardiac vibration or mechanocardiographic (MCG) noise. In this study, we present a method for improving the estimation of the respiratory muscle effort from MMGdi signals that is robust to the presence of MCG. This method is based on the calculation of the sample entropy using fixed tolerance values (fSampEn), that is, with tolerance values that are not normalized by the local standard deviation of the window analyzed. The behavior of the fSampEn parameter was tested in synthesized mechanomyographic signals, with different ratios between the amplitude of the MCG and clean mechanomyographic components. As an example of application of this technique, the use of fSampEn was explored also in recorded MMGdi signals, with different inspiratory loads. The results with both synthetic and recorded signals indicate that the entropy parameter is less affected by the MCG noise, especially at low signal-to-noise ratios. Therefore, we believe that the proposed fSampEn parameter could improve estimates of respiratory muscle effort from MMGdi signals with the presence of MCG interference.


Jané, R., (2014). Engineering Sleep Disorders: From classical CPAP devices toward new intelligent adaptive ventilatory therapy IEEE Pulse 5, (5), 29-32

Among the most common sleep disorders are those related to disruptions in airflow (apnea) or reductions in the breath amplitude (hypopnea) with or without obstruction of the upper airway (UA). One of the most important sleep disorders is obstructive sleep apnea (OSA). This sleep-disordered breathing, quantified by the apnea-hypopnea index (AHI), can produce a significant reduction of oxygen saturation and an abnormal elevation of carbon dioxide levels in the blood. Apnea and hypopnea episodes are associated with arousals and sleep fragmentation during the night and compensatory response of the autonomic nervous system.

Keywords: Biomedical engineering, Biomedical measurements, Biomedical monitoring, Breathing disorders, Medical conditions, Medical treatment, Sleep, Sleep apnea


Lozano, M., Fiz, J., Jané, R., (2014). Análisis de la intensidad de los sonidos respiratorios para el diagnóstico de la parálisis frénica unilateral CASEIB Proceedings XXXII Congreso Anual de la Sociedad Española de Ingeniería Biomédica (CASEIB 2014) , Sociedad Española de Ingeniería Biomédica (Barcelona, Spain) , 1-4

(ISR) es una medida indirecta y no invasiva de la función respiratoria, que permite comparar la actividad en los dos hemitórax de los pacientes con parálisis frénica unilateral. Se registraron los sonidos y el flujo respiratorio en 10 hombres con parálisis frénica unilateral y 10 sujetos sanos (5 hombres/5 mujeres) en posición sentada. Se colocaron 2 micrófonos de contacto en la espalda, uno a cada lado de la columna. La ISR se calculó en el rango frecuencial 70-2000 Hz a partir de la densidad espectral de potencia y para flujos entre 1,2 y 2,4 l/s. Se encontró que las diferencias en la ISR media de los dos hemitórax era significativamente mayor en los pacientes (13.5 dB) que en los sujetos sanos (2.3 dB). Además, se comprobó que esa diferencia era mayor en pacientes con un volumen espiratorio forzado en el primer segundo menor. Por lo tanto, el análisis acústico de la ISR es una técnica no invasiva muy útil para valorar la función respiratoria. Esta técnica puede mejorar la fiabilidad en el diagnóstico de la parálisis frénica unilateral así como la monitorización a largo plazo de estos pacientes.


Tellez, J. P., Herrera, S., Benito, S., Giraldo, B. F., (2014). Analysis of the breathing pattern in elderly patients using the hurst exponent applied to the respiratory flow signal Engineering in Medicine and Biology Society (EMBC) 36th Annual International Conference of the IEEE , IEEE (Chicago, USA) , 3422-3425

Due to the increasing elderly population and the extensive number of comorbidities that affect them, studies are required to determine future increments in admission to emergency departments. Some of these studies could focus on the relation between chronic diseases and breathing pattern in elderly patients. Variations in the fractal properties of respiratory signals can be associated with several diseases. To determine the relationship between these variations and breathing patterns, and to quantify the fractal properties of respiratory flow signals, we estimated the Hurst exponent (H). Detrended fluctuation analysis (DFA) and discrete wavelet transform-based estimation (DWTE) methods were applied. The estimation methods were analyzed using simulated data series generated by fractional Gaussian noise. 43 elderly patients (19 patients with a non-periodic breathing pattern - nPB, and 24 patients with a periodic breathing pattern - PB) were studied. The results were evaluated according to the length of data and the number of averaged data series used to obtain a good estimation. The DWTE method estimated the respiratory flow signals better than the DFA method, and obtained Hurst values clustered by group. We found significant differences in the H exponent (p = 0.002) between PB and nPB patients, which showed different behavior in the fractal properties.

Keywords: Discrete wavelet transforms, Diseases, Estimation, Fractals, Modulation, Senior citizens, Time series analysis


Estrada, Luis, Torres, Abel, Sarlabous, Leonardo, Fiz, Jose A., Gea, Joaquim, Martinez-Llorens, Juana, Jané, Raimon, (2014). Estimation of bilateral asynchrony between diaphragm mechanomyographic signals in patients with Chronic Obstructive Pulmonary Disease Engineering in Medicine and Biology Society (EMBC) 36th Annual International Conference of the IEEE , IEEE (Chicago, USA) , 3813-3816

The aim of the present study was to measure bilateral asynchrony in patients suffering from Chronic Obstructive Pulmonary Disease (COPD) performing an incremental inspiratory load protocol. Bilateral asynchrony was estimated by the comparison of respiratory movements derived from diaphragm mechanomyographic (MMGdi) signals, acquired by means of capacitive accelerometers placed on left and right sides of the rib cage. Three methods were considered for asynchrony evaluation: Lissajous figure, Hilbert transform and Motto's algorithm. Bilateral asynchrony showed an increase at 20, 40 and 60% (values of normalized inspiratory pressure by their maximum value reached in the last inspiratory load) while the very severe group showed and increase at 20, 40, 80, and 100 % during the protocol. These increments in the phase's shift can be due to an increase of the inspiratory load along the protocol, and also as a consequence of distress and fatigue. In summary, this work evidenced the capability to estimate bilateral asynchrony in COPD patients. These preliminary results also showed that the use of capacitive accelerometers can be a suitable sensor for recording of respiratory movement and evaluation of asynchrony in COPD patients.

Keywords: Accelerometers, Diseases, Estimation, Fatigue, IP networks, Protocols, Transforms


Correa, L.S., Giraldo, B., Correa, R., Arini, P.D., Laciar, E., (2014). Estudio de la pausa espiratoria en pacientes con enfermedades obstructivas en proceso de desconexión de la ventilación mecánica IFMBE Proceedings VI Latin American Congress on Biomedical Engineering (CLAIB 2014) , Springer (Paraná, Argentina) 49, 705-708

In this work, the flow signal Expiratory Pause (EP) temporal analysis is used in 18 patients with obstructive lung diseases going through spontaneous breathing trial at weaning process. The main objective was to identify the patients who were successfully disconnected (success group: 9 patients), and those who were not (failure and reintubated group: 9 patients). A variable selection stage was done by mean group comparison and step wise variable inclusion, leading to a 3 parameters set: EP time median; cycle time mean; and median absolute deviation of the EP maxima local number. Next, this set was used in a classifier based on linear discriminant analysis, which results in 17 patients (94.4%) correctly classified, with 88.9% of specificity (Sp) and 100% of sensitivity (Se). Finally, applying the leave-one-out cross validation method, results were 88.9% of correctly classified patients (Sp=77.8% and Se=100%). In conclusion, the proposed parameters showed a good performance and could be used to help therapists to wean patients with obstructive diseases.

Keywords: Chronic Obstructive Pulmonary Disease (COPD), Weaning, Mechanical ventilation, Expiratory pause


Giraldo, B., Chaparro, J. A., Caminal, P., Benito, S., (2014). Estudio de la potencia de la inspiración como predictor del proceso de extubación en pacientes CASEIB Proceedings XXXII Congreso Anual de la Sociedad Española de Ingeniería Biomédica (CASEIB 2014) , Sociedad Española de Ingeniería Biomédica (Barcelona, Spain) , 1-4

La extubación de pacientes asistidos mediante ventilación mecánica sigue siendo un proceso fundamental en la práctica clínica, de especial atención en las unidades de cuidados intensivos. En este estudio se propone un nuevo índice de extubación basado en la potencia de la señal de flujo respiratorio (Pi). Se estudiaron un total de 132 pacientes sometidos al proceso de destete: 94 pacientes (GE) con resultado de éxito en la prueba, y 38 pacientes (GF) que fracasaron en el proceso de destete y tuvieron que ser deconectados al ventilador mecánico. La señal de flujo respiratorio fue procesada para obtener la potencia de la fase inspiratoria, considerando las siguientes etapas: a) detección del cruce por cero, b) detección del punto de inflexión, y c) obtención de la potencia de la señal hasta dicho punto. La detección de cruce por cero se realizó utilizando un algoritmo basado en umbrales. Los puntos de inflexión fueron marcados teniendo en cuenta el cero de la segunda derivada. La potencia de la fase inspiratoria se calculó a partir de la energía de la señal desde el cruce por cero hasta el punto de máxima inflexión. El nuevo índice fue evaluado como estimador de éxito en la extubación. Los resultados fueron analizados utilizando clasificadores como regresión logística, análisis discriminante lineal, árboles de decisión, teoría bayesiana, y máquinas de soporte vectorial. Los clasificadores Bayesianos presentaron los mejores resultados con una exactitud del 87%, y sensibilidad y especificidad de 90% y 81%, respectivamente.


Téllez, J., Herrera, S., Benito, S., Giraldo, B., (2014). Estudio del patrón respiratorio en pacientes ancianos CASEIB Proceedings XXXII Congreso Anual de la Sociedad Española de Ingeniería Biomédica (CASEIB 2014) , Sociedad Española de Ingeniería Biomédica (Barcelona, Spain) , 1-4

La clínica relacionada con los pacientes ancianos, refleja una elevada incidencia de enfermedades de origen cardíaco y respiratorio. Alteraciones en el patrón respiratorio como son la respiración periódica (PB) y la respiración Cheyne-Stokes (CSR) pueden estar relacionadas con la insuficiencia cardíaca crónica (ICC). En este estudio se propuso caracterizar estos patrones respiratorios a partir de la envolvente de la señal de flujo respiratorio, aplicando técnicas de análisis frecuencial y de tiempo-frecuencia. Se estudiaron registros de 45 pacientes ancianos (25 pacientes con patrón PB y 20 pacientes con respiración no periódica (nPB)). Se analizaron los resultados considerando todas las posibles combinaciones de tipos de patrones: pacientes con patrones PB (con y sin apnea) vs nPB, y patrones CSR vs PB, CSR vs nPB y PB vs nPB. En el análisis tiempo-frecuencia se obtuvo la mayor exactitud (76.3%) con parámetros correspondientes a la variabilidad frecuencial y la desviación del pico de potencia, al comparar pacientes con patrón respiratorio nPB vs PB. Considerando segmentos de señal de 5 minutos, la potencia de pico de modulación, la variabilidad frecuencial y los rangos intercuartílicos presentaron los mejores resultados, con una exactitud del 72.8% al comparar los tres grupos (nPB, PB y CSR), y del 74.2% al comparar patrones PB vs nPB.


Estrada, L., Torres, A., Jané, R., (2014). Evaluación de la asincronía bilateral y toracoabdominal mediante señales mecanomiográficas CASEIB Proceedings XXXII Congreso Anual de la Sociedad Española de Ingeniería Biomédica (CASEIB 2014) , Sociedad Española de Ingeniería Biomédica (Barcelona, Spain) , 1-4

Este estudio tiene como objetivo evaluar la asincronía en los compartimientos torácico y abdominal, durante la realización de un protocolo respiratorio incremental de presión. La actividad mecanomiográfica fue registrada en el tronco mediante el uso de acelerómetros colocados en la parte izquierda y derecha del tórax y del abdomen. Para extraer la baja frecuencia de las señales mecanomiograficas se utilizó un método basado en la descomposición empírica en modos. Para estudiar la asincronía entre los compartimientos estudiados se utilizaron tres métodos, basados en la figura de Lissajous, la transformada de Hilbert y el algoritmo de Motto. Se observó un aumento de la asincronía toracoabdominal, con el aumento de la carga inspiratoria. Los valores de asincronía encontrados al evaluar el lado izquierdo con el derecho tanto en el diafragma como en el abdomen fueron menores de 40°, mientras que al comparar tanto el lado izquierdo como el derecho entre el tórax y el abdomen estos exhibieron valores menores a 80°. En conclusión, este trabajo demuestra que con un aumento de la carga inspiratoria puede presentarse un aumento de asincronía entre diferentes regiones del tronco. Además, el uso de acelerómetros para el registro de la dinámica respiratoria puede llegar a ser una herramienta complementaria a las actuales como la pletismografía de inductancia respiratoria, debido a su más sencilla manipulación.


Solà, J., Fiz, J.A., Torres, A., Jané, R., (2014). Evaluación de la vía aérea superior en sujetos con SAHS mediante análisis del sonido respiratorio durante vigilia CASEIB Proceedings XXXII Congreso Anual de la Sociedad Española de Ingeniería Biomédica (CASEIB 2014) , Sociedad Española de Ingeniería Biomédica (Barcelona, Spain) , 1-4

El Síndrome de Apnea-Hipopnea del Sueño (SAHS) actualmente se diagnostica mediante la Polisomnografía (PSG), una prueba cara y costosa. Se han propuesto diversas alternativas para ayudar al cribado previo de SAHS. En estudios previos demostramos que los sujetos con SAHS se pueden identificar a partir de las frecuencias de resonancia (formantes) de la respiración nocturna. En este trabajo se extiende el estudio al sonido respiratorio registrado en vigilia. Se seleccionaron diversos ciclos de inspiración y expiración consecutivas en 23 sujetos con diversos grados de SAHS durante el estado de vigilia previo a la PSG. Mediante un modelo autoregresivo (AR) se estimaron los formantes y el área transversal (CSA) de la vía. Se observa que los formantes en determinadas bandas tienen una frecuencia mayor (p<0.04) en sujetos con SAHS levemoderado, con un Índice de Apnea-Hipopnea (AHI) menor que 30, respecto a los sujetos con SAHS severo (AHI≥30). En paralelo, el área promedio de la vía aérea en las zonas con obstrucción muestra una tendencia decreciente (r=-0.498) con la severidad de la patología. Las características de los formantes, combinadas con medidas antropométricas, permiten clasificar a los sujetos con SAHS severo con una sensibilidad (especificidad) de hasta un 84.6% (88.9%). En conclusión, el sonido respiratorio registrado durante vigilia proporciona información valiosa sobre el estado de la vía aérea superior que puede ayudar a identificar un SAHS severo.


Estrada, L., Torres, A., Garcia-Casado, J., Prats-Boluda, G., Yiyao, Ye-Lin, Jané, R., (2014). Evaluation of Laplacian diaphragm electromyographic recording in a dynamic inspiratory maneuver Engineering in Medicine and Biology Society (EMBC) 36th Annual International Conference of the IEEE , IEEE (Chicago, USA) , 2201-2204

The analysis of the electromyographic signal of the diaphragm muscle (EMGdi) can provide important information for evaluating the respiratory muscular function. The EMGdi can be recorded using surface Ag/AgCl disc electrodes in monopolar or bipolar configuration. However, these non-invasive EMGdi recordings are usually contaminated by the electrocardiographic (ECG) signal. EMGdi signal can also be noninvasively recorded using concentric ring electrodes in bipolar configuration (CRE) that estimate Laplacian surface potential. Laplacian recordings increase spatial resolution and attenuate distant bioelectric interferences, such as the ECG. Thus, the objective of this work is to compare and to evaluate CRE and traditional bipolar EMGdi recordings in a healthy subject during a dynamic inspiratory maneuver with incremental inspiratory loads. In the conducted study, it was calculated the cumulative percentage of power spectrum of EMGdi recordings to determine the signal bandwidth, and the power ratio between the EMGdi signal segments with and without cardiac activity. The results of this study suggest that EMGdi acquired with CRE electrodes is less affected by the ECG interference, achieves a wider bandwidth and a higher power ratio between segments without cardiac activity and with cardiac activity.

Keywords: Bandwidth, Electric potential, Electrocardiography, Electrodes, Interference, Laplace equations, Muscles


Solà, J., Fiz, J. A., Torres, A., Jané, R., (2014). Identification of Obstructive Sleep Apnea patients from tracheal breath sound analysis during wakefulness in polysomnographic studies Engineering in Medicine and Biology Society (EMBC) 36th Annual International Conference of the IEEE , IEEE (Chicago, USA) , 4232-4235

Obstructive Sleep Apnea (OSA) is currently diagnosed by a full nocturnal polysomnography (PSG), a very expensive and time-consuming method. In previous studies we were able to distinguish patients with OSA through formant frequencies of breath sound during sleep. In this study we aimed at identifying OSA patients from breath sound analysis during wakefulness. The respiratory sound was acquired by a tracheal microphone simultaneously to PSG recordings. We selected several cycles of consecutive inspiration and exhalation episodes in 10 mild-moderate (AHI<;30) and 13 severe (AHI>=30) OSA patients during their wake state before getting asleep. Each episode's formant frequencies were estimated by linear predictive coding. We studied several formant features, as well as their variability, in consecutive inspiration and exhalation episodes. In most subjects formant frequencies were similar during inspiration and exhalation. Formant features in some specific frequency band were significantly different in mild OSA as compared to severe OSA patients, and showed a decreasing correlation with OSA severity. These formant characteristics, in combination with some anthropometric measures, allowed the classification of OSA subjects between mild-moderate and severe groups with sensitivity (specificity) up to 88.9% (84.6%) and accuracy up to 86.4%. In conclusion, the information provided by formant frequencies of tracheal breath sound recorded during wakefulness may allow identifying subjects with severe OSA.

Keywords: Correlation, Databases, Sensitivity, Sleep apnea, Speech, Synchronization


Chaparro, J. A., Giraldo, B. F., (2014). Power index of the inspiratory flow signal as a predictor of weaning in intensive care units Engineering in Medicine and Biology Society (EMBC) 36th Annual International Conference of the IEEE , IEEE (Chicago, USA) , 78-81

Disconnection from mechanical ventilation, called the weaning process, is an additional difficulty in the management of patients in intensive care units (ICU). Unnecessary delays in the discontinuation process and a weaning trial that is undertaken too early are undesirable. In this study, we propose an extubation index based on the power of the respiratory flow signal (Pi). A total of 132 patients on weaning trials were studied: 94 patients with successful trials (group S) and 38 patients who failed to maintain spontaneous breathing and were reconnected (group F). The respiratory flow signals were processed considering the following three stages: a) zero crossing detection of the inspiratory phase, b) inflection point detection of the flow curve during the inspiratory phase, and c) calculation of the signal power on the time instant indicated by the inflection point. The zero crossing detection was performed using an algorithm based on thresholds. The inflection points were marked considering the zero crossing of the second derivative. Finally, the inspiratory power was calculated from the energy contained over the finite time interval (between the instant of zero crossing and the inflection point). The performance of this parameter was evaluated using the following classifiers: logistic regression, linear discriminant analysis, the classification and regression tree, Naive Bayes, and the support vector machine. The best results were obtained using the Bayesian classifier, which had an accuracy, sensitivity and specificity of 87%, 90% and 81% respectively.

Keywords: Bayes methods, Bayesian classifier, Indexes, Logistics, Niobium, Regression tree analysis, Support vector machines, Ventilation


Sarlabous, L., Torres, A., Fiz, J.A., Gea, J., Martínez-Llorens, J.M., Jané, R., (2014). Relación entre la presión inspiratoria pico y la activación mecánica de los músculos inspiratorios durante respiración tranquila en pacientes con EPOC CASEIB Proceedings XXXII Congreso Anual de la Sociedad Española de Ingeniería Biomédica (CASEIB 2014) , Sociedad Española de Ingeniería Biomédica (Barcelona, Spain) , 1-4

En la enfermedad pulmonar obstructiva crónica (EPOC) la fuerza muscular inspiratoria (FMI) y la eficiencia mecánica de los músculos inspiratorios (EMMI) podrían verse reducidas como consecuencia de la hiperinsuflación. En este trabajo se registraron la presión inspiratoria en boca (PIpico) y la activación mecánica de los músculos inspiratorios en 10 pacientes EPOC severos y muy severos, durante respiración tranquila. Para determinar la activación mecánica de los músculos inspiratorios se empleó la señal mecanomiográfica diafragmática: MMGdi. La amplitud de la señal MMGdi fue estimada a través de índices lineales (ARV: valor rectificado medio) y no lineales (MLZ: Lempel-Ziv multiestado, y fSampEn: entropía muestral con valores de tolerancia fijos). Nuestra hipótesis es que el ratio entre PIpico, que refleja la FMI, y la amplitud de la señal MMGdi constituye una expresión de la EMMI. Los resultados obtenidos muestran ligeras diferencias entre la PIpico registrada en EPOC severos y muy severos, así como una correlación débil a moderada con los parámetros de función pulmonar y los índices estudiados. Sin embargo, mientras mayor es el grado de severidad (que supone un mayor grado de hiperinsuflación) mayor es el nivel de activación mecánica de los músculos inspiratorios. La activación mecánica de los músculos inspiratorios y la EMMI estimadas mediante MLZ estuvieron mejor correlacionadas con la función pulmonar que ARV y fSampEn. Por consiguiente, la estimación de la actividad mecánica del diafragma mediante el MLZ de la señal MMGdi podría mejorar la estimación no invasiva de la FMI y la EMMI, incluso para niveles muy bajos de esfuerzo inspiratorio.


Estrada, L., Torres, A., Sarlabous, L., Fiz, J. A., Jané, R., (2014). Respiratory rate detection by empirical mode decomposition method applied to diaphragm mechanomyographic signals Engineering in Medicine and Biology Society (EMBC) 36th Annual International Conference of the IEEE , IEEE (Chicago, USA) , 3204-3207

Non-invasive evaluation of respiratory activity is an area of increasing research interest, resulting in the appearance of new monitoring techniques, ones of these being based on the analysis of the diaphragm mechanomyographic (MMGdi) signal. The MMGdi signal can be decomposed into two parts: (1) a high frequency activity corresponding to lateral vibration of respiratory muscles, and (2) a low frequency activity related to excursion of the thoracic cage. The purpose of this study was to apply the empirical mode decomposition (EMD) method to obtain the low frequency of MMGdi signal and selecting the intrinsic mode functions related to the respiratory movement. With this intention, MMGdi signals were acquired from a healthy subject, during an incremental load respiratory test, by means of two capacitive accelerometers located at left and right sides of rib cage. Subsequently, both signals were combined to obtain a new signal which contains the contribution of both sides of thoracic cage. Respiratory rate (RR) measured from the mechanical activity (RRMmg) was compared with that measured from inspiratory pressure signal (RRP). Results showed a Pearson's correlation coefficient (r = 0.87) and a good agreement (mean bias = -0.21 with lower and upper limits of -2.33 and 1.89 breaths per minute, respectively) between RRmmg and RRP measurements. In conclusion, this study suggests that RR can be estimated using EMD for extracting respiratory movement from low mechanical activity, during an inspiratory test protocol.

Keywords: Accelerometers, Band-pass filters, Biomedical measurement, Empirical mode decomposition, Estimation, IP networks, Muscles


López Picazo, M., Solà, J., Fiz, J.A., Jané, R., (2014). Sincronización de sistemas de monitorización para el estudio de ronquidos en las distintas fases del sueño en pacientes con SAHS CASEIB Proceedings XXXII Congreso Anual de la Sociedad Española de Ingeniería Biomédica (CASEIB 2014) , Sociedad Española de Ingeniería Biomédica (Barcelona, Spain) , 1-4

El Síndrome de Apnea-Hipopnea del Sueño (SAHS) tiene una incidencia en sujetos de edad media, del 2-4% en mujeres y 4- 6% en hombres, además de múltiples consecuencias asociadas. Sin embargo, a pesar de su prevalencia, menos de un 10% de la población con este síndrome es diagnosticada. Con el objetivo de identificar qué señales debería emplear un futuro método de diagnóstico para pacientes con sospecha de SAHS más eficaz que los actuales, se sugiere un estudio en detalle de los eventos respiratorios que tienen lugar durante la noche. Para ello se parte de los estudios de monitorización del sueño realizados a pacientes con síntomas de SAHS mediante dos plataformas comerciales distintas. En primer lugar, los registros procedentes de dichos estudios se combinan y sincronizan temporalmente de una forma precisa y robusta. Una vez llevada y sincronizada toda la información a una plataforma común, el presente estudio se centra en la relación del SAHS con una nueva información, el roncograma. El concograma permite estudiar la evolución de los ronquidos según la fase de sueño. Aplicando esta medida sobre nuestra base de datos observamos como el tiempo en fase de vigilia, el tiempo en fase REM o la densidad de ronquidos en fases ligeras presentan diferencias estadísticamente significativas para pacientes con distinta severidad de SAHS.


Urra, O., Casals, A., Jané, R., (2014). Study of synergy patterns during the execution of stroke rehabilitation exercises CASEIB Proceedings XXXII Congreso Anual de la Sociedad Española de Ingeniería Biomédica (CASEIB 2014) , Sociedad Española de Ingeniería Biomédica (Barcelona, Spain) , 1-4

Stroke is a leading cause of disability, being motor impairments its major consequence. Despite rehabilitation, more than 60% of the patients report upper-limb motor dysfunction. The design of novel rehabilitation strategies requires objective measures to assess motor impairment and recovery. In a previous study, we proposed to use the synergy components of the unaffected limb as a reference to be targeted by rehabilitation, since they are proven to explain healthy motor control and to be altered after stroke. We demonstrated that healthy subjects have very similar control structures (synergies and activation vectors) in their right and left arms. Here, we investigate the existence of movement-specific control strategies. To do so, we analyze the inter-subject similarity of the healthy control structure in twelve common stroke rehabilitation exercises and we evidence that motor control is movement specific and generalizes across different subjects and their limbs. However, the similarity degree depends on the movement, suggesting that novel training protocols should purposely choose the rehabilitation exercises to ensure maximum control similarity with the reference pattern.


Urra, O., Casals, A., Jané, R., (2014). Synergy analysis as a tool to design and assess an effective stroke rehabilitation Engineering in Medicine and Biology Society (EMBC) 36th Annual International Conference of the IEEE , IEEE (Chicago, USA) , 3550-3553

The poor rehabilitation success rate, including the cases of ineffective and detrimental adaptations, make stroke a leading cause of disability. Thus, it is essential to recognize the mechanisms driving healthy motor recovery to improve such rate. Stroke alters the Synergy Architecture (SA), the modular muscle control system. So SA analysis may constitute a powerful tool to design and assess rehabilitation procedures. However, current impairment scales do not consider the patient's neuromuscular state. To gain insights into this hypothesis, we recorded multiple myoelectric signals from upper-limb muscles, in healthy subjects, while executing a set of common rehabilitation exercises. We found that SA reveals optimized motor control strategies and the positive effects of the use of visual feedback (VF) on motor control. Furthermore we demonstrate that the right and left arm's SA share the basic structure within the same subject, so we propose using the unaffected limb's SA as a reference motion pattern to be reached through rehabilitation.

Keywords: Bars, Electromyography, Motor drives, Neuromuscular, Vectors, Visualization


Lozano, M., Fiz, J. A., Jané, R., (2014). Analysis of normal and continuous adventitious sounds for the assessment of asthma IFMBE Proceedings XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013 (ed. Roa Romero, Laura M.), Springer International Publishing (London, UK) 41, 981-984

Assessment of asthma is a difficult procedure which is based on the correlation of multiple factors. A major component in the diagnosis of asthma is the assessment of BD response, which is performed by traditional spirometry. In this context, the analysis of respiratory sounds (RS) provides relevant and complementary information about the function of the respiratory system. In particular, continuous adventitious sounds (CAS), such as wheezes, contribute to assess the severity of patients with obstructive diseases. On the other hand, the intensity of normal RS is dependent on airflow level and, therefore, it changes depending on the level of obstruction. This study proposes a new approach to RS analysis for the assessment of asthmatic patients, by combining the quantification of CAS and the analysis of the changes in the normal sound intensity-airflow relationship. According to results obtained from three patients with different characteristics, the proposed technique seems more sensitive and promising for the assessment of asthma.

Keywords: Asthma, Bronchodilator response, Continuous adventitious sound, Respiratory sound intensity, Wheezes


Giraldo, B. F., Calvo, A., Martínez, B., Arcentales, A., Jané, R., Benito, S., (2014). Blood pressure variability analysis in supine and sitting position of healthy subjects IFMBE Proceedings XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013 (ed. Roa Romero, Laura M.), Springer International Publishing (London, UK) 41, 1021-1024

Blood pressure carries a great deal of information about people’s physical attributes. We analyzed the blood pressure signal in healthy subjects considering two positions, supine and sitting. 44 healthy subjects were studied. Parameters extracted from the blood pressure signal, related to time and frequency domain were used to compare the effect of postural position between supine and sitting. In time domain analysis, the time systolic interval and the time of blood pressure interval were higher in supine than in sitting position (p = 0.001 in both case). Parameters related to frequency peak, interquartile range, in frequency domain presented statistically significant difference (p < 0.0005 in both case). The blood pressure variability parameters presented smaller values in supine than in sitting position (p < 0.0005). In general, the position change of supine to sitting produces an increment in the pressure gradient inside heart, reflected in the blood pressure variability.

Keywords: Blood pressure variability, Systolic time intervals, Diastolic time intervals


Torres, A., Fiz, J. A., Jané, R., (2014). Cancellation of cardiac interference in diaphragm EMG signals using an estimate of ECG reference signal IFMBE Proceedings XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013 (ed. Roa Romero, Laura M.), Springer International Publishing (London, UK) 41, 1000-1004

The analysis of the electromyographic signal of the diaphragm muscle (EMGdi) can provide important information in order to evaluate the respiratory muscular function. However, EMGdi signals are usually contaminated by the electrocardiographic (ECG) signal. An adaptive noise cancellation (ANC) based on event-synchronous cancellation can be used to reduce the ECG interference in the recorded EMGdi activity. In this paper, it is proposed an ANC scheme for cancelling the ECG interference in EMGdi signals using only the EMGdi signal (without acquiring the ECG signal). In this case the detection of the QRS complex has been performed directly in the EMGdi signal, and the ANC algorithm must be robust to false or missing QRS detections. Furthermore, an automatic criterion to select the adaptive constant of the LMS algorithm has been proposed (

Keywords: Adaptive Canceller, EMG, Diaphragm muscle


Lambrecht, Stefan, Urra, Oiane, Grosu, Svetlana, Pérez, Soraya, (2014). Emerging rehabilitation in cerebral palsy Biosystems & Biorobotics Emerging Therapies in Neurorehabilitation (ed. Pons, José L., Torricelli, Diego), Springer Berlin Heidelberg (London, UK) 4, 23-49

Cerebral Palsy (CP) is the most frequent disability affecting children. Although the effects of CP are diverse this chapter focuses on the impaired motor control of children suffering from spastic diplegia, particularly in the lower limb. The chapter collects the most relevant techniques that are used or might be useful to overcome the current limitations existing in the diagnosis and rehabilitation of CP. Special emphasis is placed on the role that emerging technologies can play in this field. Knowing in advance the type and site of brain injury could assist the clinician in selecting the appropriate therapy. In this context, neuroimaging techniques are being recommended as an evaluation tool in children with CP; we describe a variety of imaging technologies such as Magnetic Resonance Imaging (MRI), Diffusion Tensor Imaging (DTI), etc. But creating new knowledge in itself is not enough; there must be a transfer from progress through research to advances in the clinical field. The classic therapeutic approach of CP thus hampers the optimal rehabilitation of the targeted component. Traditional therapies may be optimized if complemented with treatments. We try to collect a wide range of emerging technologies and provide some criteria to select the adequate technology based on the characteristics of the neurological injury. For example, exoskeleton based over-ground gait training is suggested to be more effective than treadmill-based gait training. So, we suggest a new point of view combining different technologies in order to provide the foundations of a rational design of the individual rehabilitation strategy.

Keywords: Cerebral palsy, Robotics, Neurostimulation, Neuroimaging, Myoelectric signals


Urra, O., Casals, A., Jané, R., (2014). Evaluating spatial characteristics of upper-limb movements from EMG signals IFMBE Proceedings XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013 (ed. Roa Romero, Laura M.), Springer International Publishing (London, UK) 41, 1795-1798

Stroke is a major cause of disability, usually causing hemiplegic damage on the motor abilities of the patient. Stroke rehabilitation seeks restoring normal motion on the affected limb. However, normality’ of movements is usually assessed by clinical and functional tests, without considering how the motor system responds to therapy. We hypothesized that electromyographic (EMG) recordings could provide useful information for evaluating the outcome of rehabilitation from a neuromuscular perspective. Four healthy subjects were asked to perform 14 different functional movements simulating the action of reaching over a table. Each movement was defined according to the starting and target positions that the subject had to connect using linear trajectories. Bipolar recordings of EMG signals were taken from biceps and triceps muscles, and spectral and temporal characteristics were extracted for each movement. Using pattern recognition techniques we found that only two EMG channels were sufficient to accurately determine the spatial characteristics of motor activity: movement direction, length and execution zone. Our results suggest that muscles may fire in a patterned way depending on the specific characteristics of the movement and that EMG signals may codify such detailed information. These findings may be of great value to quantitatively assess post-stroke rehabilitation and to compare the neuromuscular activity of the affected and unaffected limbs, from a physiological perspective. Furthermore, disturbed movements could be characterized in terms of the muscle function to identify, which is the spatial characteristic that fails, e.g. movement direction, and guide personalized rehabilitation to enhance the training of such characteristic.

Keywords: EMG, Movement spatial characteristics, Pattern recognition, Stroke rehabilitation, Upper-limb


Estrada, L., Torres, A., Garcia-Casado, J., Ye-Lin, Y., Jané, R., (2014). Evaluation of Laplacian diaphragm electromyographic recordings in a static inspiratory maneuver IFMBE Proceedings XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013 (ed. Roa Romero, Laura M.), Springer International Publishing (London, UK) 41, 977-980

Diaphragm electromyography (EMGdi) provides important information on diaphragm activity, to detect neuromuscular disorders of the most important muscle in the breathing inspiratory phase. EMGdi is habitually recorded using needles or esophageal catheters, with the implication of being invasive for patients. Surface electrodes offer an alternative for the non-invasive assessment of diaphragm activity. Ag/AgCl surface disc electrodes are used in monopolar or bipolar configuration to record EMGdi signals. On the other hand, Laplacian surface potential can be estimated by signal recording through active concentric ring electrodes. This kind of recording could reduce physiological interferences, increase the spatial selectivity and reduce orientation problems in the electrode location. The aim of this work is to compare EMGdi signals recorded simultaneously with disc electrodes in bipolar configuration and a Laplacian ring electrode over chest wall. EMGdi signal was recorded in one healthy subject during a breath hold maneuver and a static inspiratory maneuver based on Mueller’s technique. In order to estimate the covered frequency range and the degree of noise contamination in both bipolar and Laplacian EMGdi signals, the cumulative percentage of the power spectrum and the signal to noise ratio in sub-bands were determined. Furthermore, diaphragm fatigue was evaluated by means of amplitude and frequency parameters. Our findings suggest that Laplacian EMGdi recording covers a broader frequency range although with higher noise contamination compared to bipolar EMGdi recording. Finally, in Laplacian recording fatigue indexes showed a clearer trend for muscle fatigue detection and also a reduced cardiac interference, providing an alternative to bipolar recording for diaphragm fatigue studies.

Keywords: Laplacian electrode, Diaphragm muscle, Fatigue, Surface electromyography


Urra, O., Jané, R., (2014). New sleep transition indexes for describing altered sleep in SAHS IFMBE Proceedings XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013 (ed. Roa Romero, Laura M.), Springer International Publishing (London, UK) 41, 1017-1020

Traditional Sleep Structure Indexes (TSSIs) are insufficient to identify patterns of altered sleep. TSSIs mainly account for absolute time measures, but different levels of state instability may lead to similar absolute time distribution. Therefore, sleep stability remains beyond the scope of TSSIs. However, recent studies suggest that sleep disorders may be rather influenced by a breakdown in the sleep-stage switching mechanisms. In this study, we propose a set of 11 Sleep Transition Indexes (STIs) that characterize sleep fragmentation and account for the state-stability governed by the ultradian, homeostatic and circadian rhythms. We demonstrate that most of the proposed STIs are potential markers of SAHS severity, while TSSIs are not. In addition, we provide a new framework to analyze sleep disorders from the direct perspective of sleep regulatory mechanisms. In particular, our results indicate that SAHS may be influenced by a dysregulation of homeostatic rhythms but not of ultradian or circadian rhythms.

Keywords: SAHS, Sleep Transitions, Sleep Structure, Polysomnography, Hypnogram


Arizmendi, C., Viviescas, J., González, H., Giraldo, B., (2014). Patients classification on weaning trials using neural networks and wavelet transform Studies in Health Technology and Informatics (ed. Mantas, J., Househ, M. S., Hasman, A.), IOS Press Volume 202, Integrating Information Technology and Management for Quality of Care, 107-110

The determination of the optimal time of the patients in weaning trial process from mechanical ventilation, between patients capable of maintaining spontaneous breathing and patients that fail to maintain spontaneous breathing, is a very important task in intensive care unit. Wavelet Transform (WT) and Neural Networks (NN) techniques were applied in order to develop a classifier for the study of patients on weaning trial process. The respiratory pattern of each patient was characterized through different time series. Genetic Algorithms (GA) and Forward Selection were used as feature selection techniques. A classification performance of 77.00±0.06% of well classified patients, was obtained using a NN and GA combination, with only 6 variables of the 14 initials.


Morgenstern, C., Randerath, W. J., Schwaibold, M., Bolz, A., Jané, R., (2013). Feasibility of noninvasive single-channel automated differentiation of obstructive and central hypopneas with nasal airflow Respiration 85, (4), 312-318

Background: The identification of obstructive and central hypopneas is considered challenging in clinical practice. Presently, obstructive and central hypopneas are usually not differentiated or scores lack reliability due to the technical limitations of standard polysomnography. Esophageal pressure measurement is the gold-standard for identifying these events but its invasiveness deters its usage in daily practice. Objectives: To determine the feasibility and efficacy of an automatic noninvasive analysis method for the differentiation of obstructive and central hypopneas based solely on a single-channel nasal airflow signal. The obtained results are compared with gold-standard esophageal pressure scores. Methods: A total of 41 patients underwent full night polysomnography with systematic esophageal pressure recording. Two experts in sleep medicine independently differentiated hypopneas with the gold-standard esophageal pressure signal. Features were automatically extracted from the nasal airflow signal of each annotated hypopnea to train and test the automatic analysis method. Interscorer agreement between automatic and visual scorers was measured with Cohen's kappa statistic (κ). Results: A total of 1,237 hypopneas were visually differentiated. The automatic analysis achieved an interscorer agreement of κ = 0.37 and an accuracy of 69% for scorer A, κ = 0.40 and 70% for scorer B and κ = 0.41 and 71% for the agreed scores of scorers A and B. Conclusions: The promising results obtained in this pilot study demonstrate the feasibility of noninvasive single-channel hypopnea differentiation. Further development of this method may help improving initial diagnosis with home screening devices and offering a means of therapy selection and/or control.

Keywords: Central sleep hypopnea, Esophageal pressure, Home monitoring, Obstructive sleep hypopnea, Sleep disordered breathing


Sarlabous, L., Torres, A., Fiz, J. A., Morera, J., Jané, R., (2013). Index for estimation of muscle force from mechanomyography based on the Lempel-Ziv algorithm Journal of Electromyography and Kinesiology 23, (3), 548-557

The study of the amplitude of respiratory muscle mechanomyographic (MMG) signals could be useful in clinical practice as an alternative non-invasive technique to assess respiratory muscle strength. The MMG signal is stochastic in nature, and its amplitude is usually estimated by means of the average rectified value (ARV) or the root mean square (RMS) of the signal. Both parameters can be used to estimate MMG activity, as they correlate well with muscle force. These estimations are, however, greatly affected by the presence of structured impulsive noise that overlaps in frequency with the MMG signal. In this paper, we present a method for assessing muscle activity based on the Lempel-Ziv algorithm: the Multistate Lempel-Ziv (MLZ) index. The behaviour of the MLZ index was tested with synthesised signals, with various amplitude distributions and degrees of complexity, and with recorded diaphragm MMG signals. We found that this index, like the ARV and RMS parameters, is positively correlated with changes in amplitude of the diaphragm MMG components, but is less affected by components that have non-random behaviour (like structured impulsive noise). Therefore, the MLZ index could provide more information to assess the MMG-force relationship.

Keywords: Diaphragm, Electromyography, Lempel-Ziv, Mechanomyography, Muscle force, Respiratory muscles


Garde, Ainara, Voss, Andreas, Caminal, Pere, Benito, Salvador, Giraldo, Beatriz F., (2013). SVM-based feature selection to optimize sensitivity-specificity balance applied to weaning Computers in Biology and Medicine 43, (5), 533-540

Classification algorithms with unbalanced datasets tend to produce high predictive accuracy over the majority class, but poor predictive accuracy over the minority class. This problem is very common in biomedical data mining. This paper introduces a Support Vector Machine (SVM)-based optimized feature selection method, to select the most relevant features and maintain an accurate and well-balanced sensitivity–specificity result between unbalanced groups. A new metric called the balance index (B) is defined to implement this optimization. The balance index measures the difference between the misclassified data within each class. The proposed optimized feature selection is applied to the classification of patients' weaning trials from mechanical ventilation: patients with successful trials who were able to maintain spontaneous breathing after 48 h and patients who failed to maintain spontaneous breathing and were reconnected to mechanical ventilation after 30 min. Patients are characterized through cardiac and respiratory signals, applying joint symbolic dynamic (JSD) analysis to cardiac interbeat and breath durations. First, the most suitable parameters (C+,C−,

Keywords: Support vector machines, Balance index, Sensitivity-specificity balance, Cardiorespiratory interaction, Joint symbolic dynamics, Feature selection, Weaning procedure


Giraldo, B. F., Tellez, J. P., Herrera, S., Benito, S., (2013). Analysis of heart rate variability in elderly patients with chronic heart failure during periodic breathing CinC 2013 Computing in Cardiology Conference (CinC) , IEEE (Zaragoza, Spain) , 991-994

Assessment of the dynamic interactions between cardiovascular signals can provide valuable information that improves the understanding of cardiovascular control. Heart rate variability (HRV) analysis is known to provide information about the autonomic heart rate modulation mechanism. Using the HRV signal, we aimed to obtain parameters for classifying patients with and without chronic heart failure (CHF), and with periodic breathing (PB), non-periodic breathing (nPB), and Cheyne-Stokes respiration (CSR) patterns. An electrocardiogram (ECG) and a respiratory flow signal were recorded in 36 elderly patients: 18 patients with CHF and 18 patients without CHF. According to the clinical criteria, the patients were classified into the follow groups: 19 patients with nPB pattern, 7 with PB pattern, 4 with Cheyne-Stokes respiration (CSR), and 6 non-classified patients (problems with respiratory signal). From the HRV signal, parameters in the time and frequency domain were calculated. Frequency domain parameters were the most discriminant in comparisons of patients with and without CHF: PTot (p = 0.02), PLF (p = 0.022) and fpHF (p = 0.021). For the comparison of the nPB vs. CSR patients groups, the best parameters were RMSSD (p = 0.028) and SDSD (p = 0.028). Therefore, the parameters appear to be suitable for enhanced diagnosis of decompensated CHF patients and the possibility of developed periodic breathing and a CSR pattern.

Keywords: cardiovascular system, diseases, electrocardiography, frequency-domain analysis, geriatrics, medical signal processing, patient diagnosis, pneumodynamics, signal classification, Cheyne-Stokes respiration patterns, ECG, autonomic heart rate modulation mechanism, cardiovascular control, cardiovascular signals, chronic heart failure, decompensated CHF patients, dynamic interaction assessment, elderly patients, electrocardiogram, enhanced diagnosis, frequency domain parameters, heart rate variability analysis, patient classification, periodic breathing, respiratory flow signal recording, Electrocardiography, Frequency modulation, Frequency-domain analysis, Heart rate variability, Senior citizens, Standards


Sarlabous, L., Torres, A., Fiz, J. A., Jané, R., (2013). Cardiac interference reduction in diaphragmatic MMG signals during a Maintained Inspiratory Pressure Test Engineering in Medicine and Biology Society (EMBC) 35th Annual International Conference of the IEEE , IEEE (Osaka, Japan) , 3845-3848

A recursive least square (RLS) adaptive filtering algorithm for reduction of cardiac interference in diaphragmatic mecanomyographic (MMGdi) signals is addressed in this paper. MMGdi signals were acquired with a capacitive accelerometer placed between 7th and 8th intercostal spaces, on the right anterior axillary line, during a maintained inspiratory pressure test. Subjects were asked to maintain a constant inspiratory pressure with a mouthpiece connected to a closed tube (without breathing). This maneuver was repeated at five different contraction efforts: apnea (no effort), 20 cmH2O, 40 cmH2O, 60 cmH2O and maximum voluntary contraction. An adaptive noise canceller (ANC) using the RLS algorithm was applied on the MMGdi signals. To evaluate the behavior of the ANC, the MMGdi signals were analyzed in two segments: with and without cardiac interference (WCI and NCI, respectively). In both segments it was analyzed the power spectral density (PSD), and the ARV and RMS amplitude parameters for each contraction effort. With the proposed ANC algorithm the amplitude parameters of the WCI segments were reduced to a level similar to the one of the NCI segments. The obtained results showed that ANC using the RLS algorithm allows to significantly reduce the cardiac interference in MMGdi signals.


Estrada, L., Torres, A., Garcia-Casado, J., Prats-Boluda, G., Jané, R., (2013). Characterization of laplacian surface electromyographic signals during isometric contraction in biceps brachii Engineering in Medicine and Biology Society (EMBC) 35th Annual International Conference of the IEEE , IEEE (Osaka, Japan) , 535-538

Surface electromyography (sEMG) is a noninvasive technique for monitoring the electrical activity produced by the muscles. Usually, sEMG is performed by carrying out monopolar or bipolar recordings by means of conventional Ag/AgCl electrodes. In contrast, Laplacian recordings of sEMG could also be obtained by using coaxial ring electrodes. Laplacian recordings increase spatial resolution and attenuate other distant bioelectric interferences. Nevertheless, the spectral characteristics of this kind of recordings have been scarcely studied. The objective of this paper is to characterize the sEMG signals recorded with a Laplacian ring electrode and to compare them with traditional bipolar recordings with disc electrodes. Both kinds of signals were collected simultaneously in two healthy subjects during resting and sustained isometric voluntary contraction activities in biceps brachii. The conducted study computed the cumulative percentage of the power spectrum of sEMG so as to determine the energy bandwidth of the two kinds of recordings and the signal to noise ratio in different bands of the sEMG spectrum. Also, muscle fatigue, a condition when muscle force is reduced, was assessed using indexes from amplitude and frequency domain. The results of this study suggest that Laplacian sEMG has higher spectral bandwidth but a lower signal to noise ratio in comparison to bipolar sEMG. In addition, frequency fatigue indexes showed that Laplacian recording had better response than bipolar recording, which suggests that Laplacian electrode can be useful to study muscular fatigue due to better spatial resolution.


Arcentales, A., Voss, A., Caminal, P., Bayes-Genis, A., Domingo, M. T., Giraldo, B. F., (2013). Characterization of patients with different ventricular ejection fractions using blood pressure signal analysis CinC 2013 Computing in Cardiology Conference (CinC) , IEEE (Zaragoza, Spain) , 795-798

Ischemic and dilated cardiomyopathy are associated with disorders of myocardium. Using the blood pressure (BP) signal and the values of the ventricular ejection fraction, we obtained parameters for stratifying cardiomyopathy patients as low- and high-risk. We studied 48 cardiomyopathy patients characterized by NYHA ≥2: 19 patients with dilated cardiomyopathy (DCM) and 29 patients with ischemic cardiomyopathy (ICM). The left ventricular ejection fraction (LVEF) percentage was used to classify patients in low risk (LR: LVEF > 35%, 17 patients) and high risk (HR: LVEF ≤ 35%, 31 patients) groups. From the BP signal, we extracted the upward systolic slope (BPsl), the difference between systolic and diastolic BP (BPA), and systolic time intervals (STI). When we compared the LR and HR groups in the time domain analysis, the best parameters were standard deviation (SD) of 1=STI, kurtosis (K) of BPsl, and K of BPA. In the frequency domain analysis, very low frequency (VLF) and high frequency (HF) bands showed statistically significant differences in comaprisons of LR and HR groups. The area under the curve of power spectral density was the best parameter in all classifications, and particularly in the very-low-and high- frequency bands (p <; 0.001). These parameters could help to improve the risk stratification of cardiomyopathy patients.

Keywords: blood pressure measurement, cardiovascular system, diseases, medical disorders, medical signal processing, statistical analysis, time-domain analysis, BP signal, HR groups, LR groups, blood pressure signal analysis, cardiomyopathy patients, diastolic BP, dilated cardiomyopathy, frequency domain analysis, high-frequency bands, ischemic cardiomyopathy, left ventricular ejection fraction, low-frequency bands, myocardium disorders, patient characterization, power spectral density curve, standard deviation, statistical significant differences, systolic BP, systolic slope, systolic time intervals, time domain analysis, ventricular ejection fraction, Abstracts, Databases, Parameter extraction, Telecommunication standards, Time-frequency analysis


Giraldo, B. F., Chaparro, J. A., Caminal, P., Benito, S., (2013). Characterization of the respiratory pattern variability of patients with different pressure support levels Engineering in Medicine and Biology Society (EMBC) 35th Annual International Conference of the IEEE , IEEE (Osaka, Japan) , 3849-3852

One of the most challenging problems in intensive care is still the process of discontinuing mechanical ventilation, called weaning process. Both an unnecessary delay in the discontinuation process and a weaning trial that is undertaken too early are undesirable. In this study, we analyzed respiratory pattern variability using the respiratory volume signal of patients submitted to two different levels of pressure support ventilation (PSV), prior to withdrawal of the mechanical ventilation. In order to characterize the respiratory pattern, we analyzed the following time series: inspiratory time, expiratory time, breath duration, tidal volume, fractional inspiratory time, mean inspiratory flow and rapid shallow breathing. Several autoregressive modeling techniques were considered: autoregressive models (AR), autoregressive moving average models (ARMA), and autoregressive models with exogenous input (ARX). The following classification methods were used: logistic regression (LR), linear discriminant analysis (LDA) and support vector machines (SVM). 20 patients on weaning trials from mechanical ventilation were analyzed. The patients, submitted to two different levels of PSV, were classified as low PSV and high PSV. The variability of the respiratory patterns of these patients were analyzed. The most relevant parameters were extracted using the classifiers methods. The best results were obtained with the interquartile range and the final prediction errors of AR, ARMA and ARX models. An accuracy of 95% (93% sensitivity and 90% specificity) was obtained when the interquartile range of the expiratory time and the breath duration time series were used a LDA model. All classifiers showed a good compromise between sensitivity and specificity.

Keywords: autoregressive moving average processes, feature extraction, medical signal processing, patient care, pneumodynamics, signal classification, support vector machines, time series, ARX, autoregressive modeling techniques, autoregressive models with exogenous input, autoregressive moving average model, breath duration time series, classification method, classifier method, discontinuing mechanical ventilation, expiratory time, feature extraction, final prediction errors, fractional inspiratory time, intensive care, interquartile range, linear discriminant analysis, logistic regression analysis, mean inspiratory flow, patient respiratory volume signal, pressure support level, pressure support ventilation, rapid shallow breathing, respiratory pattern variability characterization, support vector machines, tidal volume, weaning trial, Analytical models, Autoregressive processes, Biological system modeling, Estimation, Support vector machines, Time series analysis, Ventilation


Lozano, M., Fiz, J. A., Jané, R., (2013). Estimation of instantaneous frequency from empirical mode decomposition on respiratory sounds analysis Engineering in Medicine and Biology Society (EMBC) 35th Annual International Conference of the IEEE , IEEE (Osaka, Japan) , 981-984

Instantaneous frequency (IF) calculated by empirical mode decomposition (EMD) provides a novel approach to analyze respiratory sounds (RS). Traditionally, RS have been analyzed using classical time-frequency distributions, such as short-time Fourier transform (STFT) or wavelet transform (WT). However, EMD has become a powerful tool for nonlinear and non-stationary data analysis. IF estimated by EMD has two major advantages: its high temporal resolution and the fact that a priori knowledge of the signal characteristics is not required. In this study, we have estimated IF by EMD on real RS signals in order to identify continuous adventitious sounds (CAS), such as wheezes, within inspiratory sounds cycles. We show that there are differences in IF distribution among frequency scales of RS signal when CAS are within RS. Therefore, a new method for RS analysis and classification may be developed by combining both EMD and IF.


Hernando, D., Alcaine, A., Pueyo, E., Laguna, P., Orini, M., Arcentales, A., Giraldo, B., Voss, A., Bayes-Genis, A., Bailon, R., (2013). Influence of respiration in the very low frequency modulation of QRS slopes and heart rate variability in cardiomyopathy patients CinC 2013 Computing in Cardiology Conference (CinC) , IEEE (Zaragoza, Spain) , 117-120

This work investigates the very low frequency (VLF) modulation of QRS slopes and heart rate variability (HRV). Electrocardiogram (ECG) and respiratory flow signal were acquired from patients with dilated cardiomyopathy and ischemic cardiomyopathy. HRV as well as the upward QRS slope (IUS) and downward QRS slope (IDS) were extracted from the ECG. The relation between HRV and QRS slopes in the VLF band was measured using ordinary coherence in 5-minute segments. Partial coherence was then used to remove the influence that respiration simultaneously exerts on HRV and QRS slopes. A statistical threshold was determined, below which coherence values were considered not to represent a linear relation. 7 out of 276 segments belonging to 5 out of 29 patients for IUS and 10 segments belonging to 5 patients for IDS presented a VLF modulation in QRS slopes, HRV and respiration. In these segments spectral coherence was statistically significant, while partial coherence decreased, indicating that the coupling HRV and QRS slopes was related to respiration. 4 segments had a partial coherence value below the threshold for IUS, 3 segments for IDS. The rest of the segments also presented a notable decrease in partial coherence, but still above the threshold, which means that other non-linearly effects may also affect this modulation.

Keywords: diseases, electrocardiography, feature extraction, medical signal processing, pneumodynamics, statistical analysis, ECG, QRS slopes, cardiomyopathy patients, dilated cardiomyopathy, electrocardiogram, feature extraction, heart rate variability, ischemic cardiomyopathy, ordinary coherence, partial coherence value, respiration, respiratory flow signal acquisition, spectral coherence, statistical threshold, time 5 min, very low frequency modulation, Coherence, Educational institutions, Electrocardiography, Frequency modulation, Heart rate variability


Gonzalez, H., Acevedo, H., Arizmendi, C., Giraldo, B. F., (2013). Methodology for determine the moment of disconnection of patients of the mechanical ventilation using discrete wavelet transform Complex Medical Engineering (CME) 2013 ICME International Conference , IEEE (Beijing, China) , 483-486

The process of weaning from mechanical ventilation is one of the challenges in intensive care units. 66 patients under extubation process (T-tube test) were studied: 33 patients with successful trials and 33 patients who failed to maintain spontaneous breathing and were reconnected. Each patient was characterized using 7 time series from respiratory signals, and for each serie was evaluated the discrete wavelet transform. It trains a neural network for discriminating between patients from the two groups.

Keywords: discrete wavelet transforms, neural nets, patient treatment, pneumodynamics, time series, ventilation, T-tube test, discrete wavelet transform, extubation process, intensive care units, mechanical ventilation, moment of disconnection, neural network, patients, respiratory signals, spontaneous breathing, time series, weaning, Mechanical Ventilation, Neural Networks, Time series from respiratory signals, Wavelet Transform


Jané, R., Lazaro, J., Ruiz, P., Gil, E., Navajas, D., Farre, R., Laguna, P., (2013). Obstructive Sleep Apnea in a rat model: Effects of anesthesia on autonomic evaluation from heart rate variability measures CinC 2013 Computing in Cardiology Conference (CinC) , IEEE (Zaragoza, Spain) , 1011-1014

Rat model of Obstructive Sleep Apnea (OSA) is a realistic approach for studying physiological mechanisms involved in sleep. Rats are usually anesthetized and autonomic nervous system (ANS) could be blocked. This study aimed to assess the effect of anesthesia on ANS activity during OSA episodes. Seven male Sprague-Dawley rats were anesthetized intraperitoneally with urethane (1g/kg). The experiments were conducted applying airway obstructions, simulating 15s-apnea episodes for 15 minutes. Five signals were acquired: respiratory pressure and flow, SaO2, ECG and photoplethysmography (PPG). In total, 210 apnea episodes were studied. Normalized power spectrum of Pulse Rate Variability (PRV) was analyzed in the Low Frequency (LF) and High Frequency (HF) bands, for each episode in consecutive 15s intervals (before, during and after the apnea). All episodes showed changes in respiratory flow and SaO2 signal. Conversely, decreases in the amplitude fluctuations of PPG (DAP) were not observed. Normalized LF presented extremely low values during breathing (median=7,67%), suggesting inhibition of sympathetic system due to anesthetic effect. Subtle increases of LF were observed during apnea. HRV and PPG analysis during apnea could be an indirect tool to assess the effect and deep of anesthesia.

Keywords: electrocardiography, fluctuations, medical disorders, medical signal detection, medical signal processing, neurophysiology, photoplethysmography, pneumodynamics, sleep, ECG, SaO2 flow, SaO2 signal, airway obstructions, amplitude fluctuations, anesthesia effects, anesthetized nervous system, autonomic evaluation, autonomic nervous system, breathing, heart rate variability, high-frequency bands, low-frequency bands, male Sprague-Dawley rats, normalized power spectrum, obstructive sleep apnea, photoplethysmography, physiological mechanisms, pulse rate variability, rat model, respiratory flow, respiratory pressure, signal acquisition, sympathetic system inhibition, time 15 min, time 15 s, Abstracts, Atmospheric modeling, Computational modeling, Electrocardiography, Rats, Resonant frequency


Giraldo, B. F., Tellez, J. P., Herrera, S., Benito, S., (2013). Study of the oscillatory breathing pattern in elderly patients Engineering in Medicine and Biology Society (EMBC) 35th Annual International Conference of the IEEE , IEEE (Osaka, Japan) , 5228-5231

Some of the most common clinical problems in elderly patients are related to diseases of the cardiac and respiratory systems. Elderly patients often have altered breathing patterns, such as periodic breathing (PB) and Cheyne-Stokes respiration (CSR), which may coincide with chronic heart failure. In this study, we used the envelope of the respiratory flow signal to characterize respiratory patterns in elderly patients. To study different breathing patterns in the same patient, the signals were segmented into windows of 5 min. In oscillatory breathing patterns, frequency and time-frequency parameters that characterize the discriminant band were evaluated to identify periodic and non-periodic breathing (PB and nPB). In order to evaluate the accuracy of this characterization, we used a feature selection process, followed by linear discriminant analysis. 22 elderly patients (7 patients with PB and 15 with nPB pattern) were studied. The following classification problems were analyzed: patients with either PB (with and without apnea) or nPB patterns, and patients with CSR versus PB, CSR versus nPB and PB versus nPB patterns. The results showed 81.8% accuracy in the comparisons of nPB and PB patients, using the power of the modulation peak. For the segmented signal, the power of the modulation peak, the frequency variability and the interquartile ranges provided the best results with 84.8% accuracy, for classifying nPB and PB patients.

Keywords: cardiovascular system, diseases, feature extraction, geriatrics, medical signal processing, oscillations, pneumodynamics, signal classification, time-frequency analysis, Cheyne-Stokes respiration, apnea, cardiac systems, chronic heart failure, classification problems, discriminant band, diseases, elderly patients, feature selection process, frequency variability, interquartile ranges, linear discriminant analysis, nonperiodic breathing, oscillatory breathing pattern, periodic breathing, respiratory How signal, respiratory systems, signal segmentation, time 5 min, time-frequency parameters, Accuracy, Aging, Frequency modulation, Heart, Senior citizens, Time-frequency analysis


Fiz, J. A., Jané, R., (2012). Snoring Analysis. A Complex Question Journal of Sleep Disorders: Treatment & Care 1, (1), 1-3

The snore is a breathing sound that originates during sleep, either nocturnal or diurnal. Many procedures have been used for its analysis, from simple interrogation, going through acoustic methods that have been developed thanks to the advance of biomedical techniques in recent years. So far a procedure homologated by different laboratories for its study doesn’t exist. The present editorial describes the current state of the art in the snoring analysis procedures.

Keywords: Snoring, Sleep apnea, OSAS


Mesquita, J., Solà, J., Fiz, J. A., Morera, J., Jané, R., (2012). All night analysis of time interval between snores in subjects with sleep apnea hypopnea syndrome Medical and Biological Engineering and Computing 50, (4), 373-381

Sleep apnea-hypopnea syndrome (SAHS) is a serious sleep disorder, and snoring is one of its earliest and most consistent symptoms. We propose a new methodology for identifying two distinct types of snores: the so-called non-regular and regular snores. Respiratory sound signals from 34 subjects with different ranges of Apnea-Hypopnea Index (AHI = 3.7-109.9 h -1) were acquired. A total number of 74,439 snores were examined. The time interval between regular snores in short segments of the all night recordings was analyzed. Severe SAHS subjects show a shorter time interval between regular snores (p = 0.0036, AHI cp: 30 h -1) and less dispersion on the time interval features during all sleep. Conversely, lower intra-segment variability (p = 0.006, AHI cp: 30 h -1) is seen for less severe SAHS subjects. Features derived from the analysis of time interval between regular snores achieved classification accuracies of 88.2 % (with 90 % sensitivity, 75 % specificity) and 94.1 % (with 94.4 % sensitivity, 93.8 % specificity) for AHI cut-points of severity of 5 and 30 h -1, respectively. The features proved to be reliable predictors of the subjects' SAHS severity. Our proposed method, the analysis of time interval between snores, provides promising results and puts forward a valuable aid for the early screening of subjects suspected of having SAHS.

Keywords: Sleep apnea, Snore sounds, Snore time interval


Solà, J., Fiz, J. A., Morera, J., Jané, R., (2012). Multiclass classification of subjects with sleep apnoea-hypopnoea syndrome through snoring analysis Medical Engineering and Physics 34, (9), 1213-1220

The gold standard for diagnosing sleep apnoea-hypopnoea syndrome (SAHS) is polysomnography (PSG), an expensive, labour-intensive and time-consuming procedure. Accordingly, it would be very useful to have a screening method to allow early assessment of the severity of a subject, prior to his/her referral for PSG. Several differences have been reported between simple snorers and SAHS patients in the acoustic characteristics of snoring and its variability. In this paper, snores are fully characterised in the time domain, by their sound intensity and pitch, and in the frequency domain, by their formant frequencies and several shape and energy ratio measurements. We show that accurate multiclass classification of snoring subjects, with three levels of SAHS, can be achieved on the basis of acoustic analysis of snoring alone, without any requiring information on the duration or the number of apnoeas. Several classification methods are examined. The best of the approaches assessed is a Bayes model using a kernel density estimation method, although good results can also be obtained by a suitable combination of two binary logistic regression models. Multiclass snore-based classification allows early stratification of subjects according to their severity. This could be the basis of a single channel, snore-based screening procedure for SAHS.

Keywords: Bayes classifier, Kernel density estimation, Sleep apnoea, Snoring


Lozano, M., Fiz, J.A., Jané, R., (2012). Análisis multicanal de sonidos respiratorios en acústica pulmonar: aplicación clínica en pacientes asmáticos Libro de Actas XXX CASEIB 2012 XXX Congreso Anual de la Sociedad Española de Ingeniería Biomédica (CASEIB2012) , Sociedad Española de Ingeniería Biomédica (San Sebastián, Spain) , 1-4

En este trabajo se ha visto que la evaluación de la potencia media de las señales de sonidos respiratorios antes y después de aplicar un fármaco broncodilatador puede reportar información importante sobre el estado y los cambios producidos en el sistema respiratorio. Se ha realizado y puesto a punto un protocolo de registro multicanal de acústica pulmonar mediante la colocación de 5 micrófonos de contacto: uno traqueal y cuatro micrófonos colocados en el tórax posterior. Mediante el análisis de las curvas intensidad-flujo respiratorio se han observado cambios significativos de intensidad a niveles de flujo elevados y en pacientes con una PBD negativa. Es en este grupo de pacientes, no respondedores, donde la técnica propuesta puede aportar información de interés clínico complementaria a la proporcionada por la espirometría clásica.


Giraldo, B.F., Gaspar, B.W., Caminal, P., Benito, S., (2012). Analysis of roots in ARMA model for the classification of patients on weaning trials Engineering in Medicine and Biology Society (EMBC) 34th Annual International Conference of the IEEE , IEEE (San Diego, USA) , 698-701

One objective of mechanical ventilation is the recovery of spontaneous breathing as soon as possible. Remove the mechanical ventilation is sometimes more difficult that maintain it. This paper proposes the study of respiratory flow signal of patients on weaning trials process by autoregressive moving average model (ARMA), through the location of poles and zeros of the model. A total of 151 patients under extubation process (T-tube test) were analyzed: 91 patients with successful weaning (GS), 39 patients that failed to maintain spontaneous breathing and were reconnected (GF), and 21 patients extubated after the test but before 48 hours were reintubated (GR). The optimal model was obtained with order 8, and statistical significant differences were obtained considering the values of angles of the first four poles and the first zero. The best classification was obtained between GF and GR, with an accuracy of 75.3% on the mean value of the angle of the first pole.

Keywords: Analytical models, Biological system modeling, Computational modeling, Estimation, Hospitals, Poles and zeros, Ventilation, Autoregressive moving average processes, Patient care, Patient monitoring, Pneumodynamics, Poles and zeros, Ventilation, ARMA model, T-tube test, Autoregressive moving average model, Extubation process, Mechanical ventilation, Optimal model, Patient classification, Respiratory flow signal, Roots, Spontaneous breathing, Weaning trials


Urra, O., Fiz, J.A., Abad, J., Jané, R., (2012). Beyond the reach of AHI: identifying key markers for improved systematic diagnosis of SAHS Libro de Actas XXX CASEIB 2012 XXX Congreso Anual de la Sociedad Española de Ingeniería Biomédica (CASEIB2012) , Sociedad Española de Ingeniería Biomédica (San Sebastián, Spain) , 1-4

Chaparro, J., Giraldo, B.F., Caminal, P., Benito, S., (2012). Comportamiento de parámetros del patrón respiratorio en clasificadores para la predicción del proceso weaning Libro de Actas XXX CASEIB 2012 XXX Congreso Anual de la Sociedad Española de Ingeniería Biomédica (CASEIB2012) , Sociedad Española de Ingeniería Biomédica (San Sebastián, Spain) , 1-4

Garde, A., Laguna, P., Giraldo, B.F., Jané, R., Sörnmo, L., (2012). Ensemble-based time alignment of biomedical signals Proceedings BSI 2012 7th International Workshop on Biosignal Interpretation (BSI 2012) , IEEE (Como, Italy) W3: METHODS FOR BIOMEDICAL SIGNAL PROCESSING ENHANCEMENT, 307-310

In this paper, the problem of time alignment is revisited by adopting an ensemble-based approach with all signals jointly aligned. It is shown that the maximization of an eigenvalue ratio is synonymous to maximizing the signal-to-jitter-and-noise ratio. Since optimization of this criterion is extremely time consuming, a relaxed optimization procedure is introduced which converges much more quickly. Using simulations based on respiratory flow signals, the results suggest that the time delay error variance of the new method is much lower than that obtained with the well-known Woody’s method.

Keywords: Time alignment, Signal ensemble, Subsample precision, Eigenvalue decomposition


Garde, A., Giraldo, B.F., Jané, R., Latshang, T.D., Turk, A.J., Hess, T., Bosch, M-.M., Barthelmes, D., Hefti, J.P., Maggiorini, M., Hefti, U., Merz, T.M., Schoch, O.D., Bloch, K.E., (2012). Estudio de la respiración periódica en el ascenso a altitudes extremas a partir de la señal de volumen respiratorio Libro de Actas XXX CASEIB 2012 XXX Congreso Anual de la Sociedad Española de Ingeniería Biomédica (CASEIB2012) , Sociedad Española de Ingeniería Biomédica (San Sebastián, Spain) , 1-4

La respiración periódica (PB) a gran altitud comparte aspectos fisiopatológicos con la apnea, la respiración Cheyne-Stokes y la PB en pacientes con insuficiencia cardiaca. Cuantificar las inestabilidades del control respiratorio puede proporcionar información relevante de los mecanismos fisiológicos que las producen, y ayudar en las actuaciones terapéuticas. Bajo la hipótesis de que en altitudes extremas la PB puede aparecer incluso durante actividad física, el objetivo es identificar la PB y evaluar el efecto de aclimatación a partir de la caracterización del patrón respiratorio mediante la señal de volumen respiratorio. Se analizaron los datos obtenidos de 34 montañeros sanos ascendiendo al Muztagh Ata, China (7,546m). Sus señales se etiquetaron visualmente como, respiración periódica (PB=40) y no periódica (nPB=371). El patrón respiratorio se caracterizó a partir de parámetros extraídos de la densidad espectral de potencia de la señal de volumen respiratorio. Los mejores resultados clasificando PB y nPB se obtuvieron con Pm (potencia de modulación) y R (ratio entre potencia de modulación y respiración) con una precisión del 80.3% y un área bajo la curva de 84.5%. SaO2 y el número de ciclos periódicos de respiración aumentaron significativamente con la aclimatación (p-valor<0.05). A menor SaO2 se observó una mayor Pm y frecuencia respiratoria, (correlación negativa, p-valor<0.01), y una mayor Pm en periodos etiquetados como PB con > 5 ciclos respiratorios periódicos, (correlación positiva, p-valor<0.01). Estos resultados demuestran que la caracterización espectral de la señal de volumen respiratorio permite identificar los efectos de la hipoxia hipobárica en el control de la respiración.


Torres, A., Sarlabous, L., Fiz, J.A., Jané, R., (2012). Evaluación de diferentes algoritmos adaptativos para la atenuación de la interferencia cardiaca en señales mecanomiográficas simuladas Libro de Actas XXX CASEIB 2012 XXX Congreso Anual de la Sociedad Española de Ingeniería Biomédica (CASEIB2012) , Sociedad Española de Ingeniería Biomédica (San Sebastián, Spain) , 1-4

El estudio de la señal mecanomiográfica del músculo diafragma (MMGdi) es una técnica utilizada para evaluar el esfuerzo muscular respiratorio. El estudio de la relación entre los parámetros de amplitud y frecuencia de esta señal con el esfuerzo respiratorio realizado es de gran interés para investigadores y médicos debido a su potencial de diagnóstico sobre la función muscular respiratoria. Las señales MMGdi se ven afectas por una componente interferente correspondiente a la actividad vibratoria cardíaca o interferencia mecanocardiográfica (MCG). Para reducir o atenuar esta actividad se puede utilizar una cancelación adaptativa de interferencias (CAI). En este trabajo se ha evaluado el esquema de CAI propuesto mediante una señal MMGdi sintética generada con amplitud y frecuencia controlada a la que se le ha añadido ruido MCG real adquirido durante apnea. El coeficiente de correlación de Pearson (r) entre la amplitud y la frecuencia teóricas, y la amplitud y la frecuencia evaluadas mediante el RMS y la frecuencia media del espectro, respectivamente, disminuye considerablemente cuando se añade el ruido cardíaco a la señal MMGdi sintética: pasa de 0.95 a 0.87 para la amplitud, y de 0.97 a 0.76 para la frecuencia. Con los algoritmos de CAI propuestos el efecto del ruido MCG sobre la actividad MMGdi se reduce considerablemente (r de 0.93 para la amplitud y 0.97 para la frecuencia media). El método de CAI propuesto en este trabajo es una técnica adecuada para atenuar la interferencia MCG en señales MMGdi.


Sarlabous, L., Torres, A., Fiz, J. A., Morera, J., Jané, R., (2012). Evaluation and adaptive attenuation of the cardiac vibration interference in mechanomyographic signals Engineering in Medicine and Biology Society (EMBC) 34th Annual International Conference of the IEEE , IEEE (San Diego, USA) , 3400-3403

The study of the mechanomyographic signal of the diaphragm muscle (MMGdi) is a promising technique in order to evaluate the respiratory muscles effort. The relationship between amplitude and frequency parameters of this signal with the respiratory effort performed during respiration is of great interest for researchers and physicians due to its diagnostic potentials. However, MMGdi signals are frequently contaminated by a cardiac vibration or mechanocardiographic (MCG) signal. An adaptive noise cancellation (ANC) can be used to reduce the MCG interference in the recorded MMGdi activity. In this paper, it is evaluated the proposed ANC scheme by means of a synthetic MMGdi signal with a controlled MCG interference. The Pearson's correlation coefficient (PCC) between both root mean square (RMS) and mean frequency (fm) of the synthetic MMGdi signal are considerably reduced with the presence of cardiac vibration noise (from 0.95 to 0.87, and from 0.97 to 0.76, respectively). With the ANC algorithm proposed the effect of the MCG noise on the amplitude and frequency of MMG parameters is reduced considerably (PCC of 0.93 and 0.97 for the RMS and fm, respectively). The ANC method proposed in this work is an interesting technique to attenuate the cardiac interference in respiratory MMG signals. Further investigation should be carried out to evaluate the performance of the ANC algorithm in real MMGdi signals.

Keywords: Adaptive filters, Frequency modulation, Interference, Muscles, Noise cancellation, Vibrations, Cardiology, Medical signal processing, Muscle, Signal denoising, ANC algorithm, MCG interference, Pearson correlation coefficient, Adaptive noise cancellation, Cardiac vibration interference, Cardiac vibration noise, Diaphragm muscle, Mechanocardiographic signal, Mechanomyographic signals, Respiratory muscles effort


Chaparro, J.A., Giraldo, B.F., Caminal, P., Benito, S., (2012). Performance of respiratory pattern parameters in classifiers for predict weaning process Engineering in Medicine and Biology Society (EMBC) 34th Annual International Conference of the IEEE , IEEE (San Diego, USA) , 4349-4352

Weaning trials process of patients in intensive care units is a complex clinical procedure. 153 patients under extubation process (T-tube test) were studied: 94 patients with successful trials (group S), 38 patients who failed to maintain spontaneous breathing and were reconnected (group F), and 21 patients with successful test but that had to be reintubated before 48 hours (group R). The respiratory pattern of each patient was characterized through the following time series: inspiratory time (TI), expiratory time (TE), breathing cycle duration (TTot), tidal volume (VT), inspiratory fraction (TI/TTot), half inspired flow (VT/TI), and rapid shallow index (f/VT), where f is respiratory rate. Using techniques as autoregressive models (AR), autoregressive moving average models (ARMA) and autoregressive models with exogenous input (ARX), the most relevant parameters of the respiratory pattern were obtained. We proposed the evaluation of these parameters using classifiers as logistic regression (LR), linear discriminant analysis (LDA), support vector machines (SVM) and classification and regression tree (CART) to discriminate between patients from groups S, F and R. An accuracy of 93% (98% sensitivity and 82% specificity) has been obtained using CART classification.

Keywords: Accuracy, Indexes, Logistics, Regression tree analysis, Support vector machines, Time series analysis, Autoregressive moving average processes, Medical signal processing, Pattern classification, Pneumodynamics, Regression analysis, Sensitivity, Signal classification, Support vector machines, Time series, SVM, T-tube testing, Autoregressive models-with-exogenous input, Autoregressive moving average models, Breathing cycle duration, Classification-and-regression tree, Expiratory time, Extubation process, Half inspired flow, Inspiratory fraction, Inspiratory time, Intensive care units, Linear discriminant analysis, Logistic regression, Rapid shallow index, Respiratory pattern parameter performance, Sensitivity, Spontaneous breathing, Support vector machines, Tidal volume, Time 48 hr, Time series, Weaning process classifiers


Garde, A., Giraldo, B.F., Jané, R., Latshang, T.D., Turk, A.J., Hess, T., Bosch, M-.M., Barthelmes, D., Hefti, J.P., Maggiorini, M., Hefti, U., Merz, T.M., Schoch, O.D., Bloch, K.E., (2012). Periodic breathing during ascent to extreme altitude quantified by spectral analysis of the respiratory volume signal Engineering in Medicine and Biology Society (EMBC) 34th Annual International Conference of the IEEE , IEEE (San Diego, USA) , 707-710

High altitude periodic breathing (PB) shares some common pathophysiologic aspects with sleep apnea, Cheyne-Stokes respiration and PB in heart failure patients. Methods that allow quantifying instabilities of respiratory control provide valuable insights in physiologic mechanisms and help to identify therapeutic targets. Under the hypothesis that high altitude PB appears even during physical activity and can be identified in comparison to visual analysis in conditions of low SNR, this study aims to identify PB by characterizing the respiratory pattern through the respiratory volume signal. A number of spectral parameters are extracted from the power spectral density (PSD) of the volume signal, derived from respiratory inductive plethysmography and evaluated through a linear discriminant analysis. A dataset of 34 healthy mountaineers ascending to Mt. Muztagh Ata, China (7,546 m) visually labeled as PB and non periodic breathing (nPB) is analyzed. All climbing periods within all the ascents are considered (total climbing periods: 371 nPB and 40 PB). The best crossvalidated result classifying PB and nPB is obtained with Pm (power of the modulation frequency band) and R (ratio between modulation and respiration power) with an accuracy of 80.3% and area under the receiver operating characteristic curve of 84.5%. Comparing the subjects from 1st and 2nd ascents (at the same altitudes but the latter more acclimatized) the effect of acclimatization is evaluated. SaO2 and periodic breathing cycles significantly increased with acclimatization (p-value <; 0.05). Higher Pm and higher respiratory frequencies are observed at lower SaO2, through a significant negative correlation (p-value <; 0.01). Higher Pm is observed at climbing periods visually labeled as PB with >; 5 periodic breathing cycles through a significant positive correlation (p-value <; 0.01). Our data demonstrate that quantification of the respiratory volum- signal using spectral analysis is suitable to identify effects of hypobaric hypoxia on control of breathing.

Keywords: Frequency domain analysis, Frequency modulation, Heart, Sleep apnea, Ventilation, Visualization, Cardiology, Medical disorders, Medical signal processing, Plethysmography, Pneumodynamics, Sensitivity analysis, Sleep, Spectral analysis, Cheyne-Stokes respiration, Climbing periods, Dataset, Heart failure patients, High altitude PB, High altitude periodic breathing, Hypobaric hypoxia, Linear discriminant analysis, Pathophysiologic aspects, Physical activity, Physiologic mechanisms, Power spectral density, Receiver operating characteristic curve, Respiratory control, Respiratory frequency, Respiratory inductive plethysmography, Respiratory pattern, Respiratory volume signal, Sleep apnea, Spectral analysis, Spectral parameters


Sarlabous, L., Torres, A., Fiz, J.A., Jané, R., (2012). Reducción de interferencia cardíaca en señales MMG diafragmáticas registradas durante un protocolo de carga incremental sostenida mediante el algoritmo RLS Libro de Actas XXX CASEIB 2012 XXX Congreso Anual de la Sociedad Española de Ingeniería Biomédica (CASEIB2012) , Sociedad Española de Ingeniería Biomédica (San Sebastián, Spain) , 1-4

En este trabajo se aplicó el filtrado adaptativo empleando el algoritmo RLS para reducir la interferencia de origen cardíaco en las señales mecanomiográficas diafragmáticas (MMGdi) registras durante un protocolo de carga incremental sostenida. La señal MMGdi fue dividida en tramos con y sin ruido cardíaco, CRC y SRC, respectivamente. En cada tramo se estudio el comportamiento de la densidad espectral de potencia (DEP), y los parámetros de amplitud RMS y ARV para cada una de las cargas inspiratorias que conforman el test. Los resultados obtenidos, empleando filtro adaptativo de orden =50, con el algoritmo RLS y valores de - = 1, permiten reducir considerablemente la interferencia cardíaca en las señales MMGdi.


Mesquita, J., Poree, F., Carrault, G., Fiz, J. A., Abad, J., Jané, R., (2012). Respiratory and spontaneous arousals in patients with Sleep Apnea Hypopnea Syndrome Engineering in Medicine and Biology Society (EMBC) 34th Annual International Conference of the IEEE , IEEE (San Diego, USA) , 6337-6340

Sleep in patients with Sleep Apnea-Hypopnea Syndrome (SAHS) is frequently interrupted with arousals. Increased amounts of arousals result in shortening total sleep time and repeated sleep-arousal change can result in sleep fragmentation. According to the American Sleep Disorders Association (ASDA) an arousal is a marker of sleep disruption representing a detrimental and harmful feature for sleep. The nature of arousals and its role on the regulation of the sleep process raises controversy and has sparked the debate in the last years. In this work, we analyzed and compared the EEG spectral content of respiratory and spontaneous arousals on a database of 45 SAHS subjects. A total of 3980 arousals (1996 respiratory and 1984 spontaneous) were analyzed. The results showed no differences between the spectral content of the two kinds of arousals. Our findings raise doubt as to whether these two kinds of arousals are truly triggered by different organic mechanisms. Furthermore, they may also challenge the current beliefs regarding the underestimation of the importance of spontaneous arousals and their contribution to sleep fragmentation in patients suffering from SAHS.

Keywords: Adaptive filters, Correlation, Databases, Electroencephalography, Hospitals, Sleep apnea, Electroencephalography, Medical signal processing, Pneumodynamics, Sleep, EEG spectral content, Organic mechanism, Respiratory, Sleep apnea hypopnea syndrome, Sleep fragmentation, Spectral content, Spontaneous arousal


Yue, J. J., Morgenstern, R., Morgenstern, C., Lauryssen, C., (2011). Shape memory hydrogels - A novel material for treating age-related degenerative conditions of the Spine European Musculoskeletal Review 6, (3), 184-188

Hydrogels are water-insoluble hydrophilic polymers used in a wide range of medical products such as, drug delivery, tissue replacement, heart surgery, gynaecology, ophthalmology, plastic surgery and orthopaedic surgery. These polymers exhibit low toxicity, reduced tissue adherence, and are highly biocompatible. A class of hydrogels, hydrolysed polyacrylonitriles, possess unique shape memory properties, which, when combined with biodurability, mechanical strength and viscoelasticity make them ideal for treating certain degenerative conditions of the spine. Animal and other in vitro studies have shown that the hydrogel is biocompatible and well tolerated by host tissues. This article focuses on two specific indications in spine surgery that demonstrate the potential of hydrogel-based technology to provide significant treatment advantages.

Keywords: Biocompatibility, Degenerative disc disease, Hydrolysed polyacrylonitrile, Minimally invasive surgery, Shape memory hydrogel, Spinal stenosis


Morgenstern, R., Morgenstern, C., Jané, R., Lee, S. H., (2011). Usefulness of an expandable interbody spacer for the treatment of foraminal stenosis in extremely collapsed disks preliminary clinical experience with endoscopic posterolateral transforaminal approach Journal of Spinal Disorders & Techniques 24, (8), 485-491

Study Design: Clinical series of patients with degenerative disk disease undergoing an endoscopic posterolateral transforaminal procedure that used a reaming foraminoplasty technique to enlarge the foramen coupled with insertion of the B-Twin expandable spacer. Objectives: This retrospective analysis of 107 consecutive patients sought to assess the outcome of this surgical procedure. Summary of Background Data: Reamed endoscopic foraminoplasty under direct endoscopic vision has been shown to be suitable for extremely collapsed disks (> 50% total disk height) despite the difficult access, especially at L5-S1. The authors tried to investigate the efficacy of an expandable spacer being inserted by the endoscopic transforaminal approach to solve foraminal stenosis without bone fusion techniques. Methods: The procedure consists of bone reaming under direct endoscopic control to wide the foramen followed by insertion of the B-Twin expandable device as a disk spacer to restore partially or to maintain the height of the collapsed disk. Outcome measures included visual analog scale (VAS) for pain, the Oswestry Disability Index (ODI) for functional disability, and radioimaging studies. Results: Mean follow-up was 27.2 months. Clinical outcome was considered excellent in 64 patients, good in 25, fair in 10, and poor in 8. Results were similar in single and double B-Twin spacer insertions. Postoperative mean values for VAS and ODI scores improved significantly as compared with preoperative data. Mean VAS and ODI scores were significantly higher in patients with fair or poor results than in those with excellent or good outcome. In 2 cases, clear signs of end plate bone resorption in the control computed tomographic scans at 6 months and 12 months leading to a substantial loss of disk height were documented. Conclusions: This preliminary study has shown the efficacy of an endoscopic surgical technique for the treatment of foraminal stenosis in extremely collapsed disks.

Keywords: Foraminal stenosis, B-twin expandable spacer, Endoscopic foraminoplasty, Minimally invasive surgery, Surgical technique, Spinal spacer, Lumbar, Diskectomy, Fusion, Discectomy


Fiz, José Antonio, Solà, J., Jané, Raimon, (2011). Métodos de análisis del ronquido Medicina Clínica 137, (1), 36-42

El ronquido es un sonido respiratorio que se produce durante el sueño, ya sea nocturno o diurno. El ronquido puede ser inspiratorio, espiratorio o puede ocupar todo el ciclo respiratorio. Tiene su origen en la vibración de los diferentes tejidos de la vía aérea superior. Se han descrito numerosos métodos para analizarlo, desde el simple interrogatorio, pasando por cuestionarios estándares, hasta llegar a los métodos acústicos más sofisticados, que se han desarrollado gracias al gran avance de las técnicas biomédicas en los últimos años. El presente trabajo describe el estado del arte actual en los procedimientos de análisis del ronquido.

Keywords: Ronquido, Apnea del sueño, Síndrome de apnea-hipoapnea del sueño, Snoring, Sleep apnea, Sleep Apnea and Hipoapnea Syndrome


Garde, A., Giraldo, B.F., Sornmo, L., Jané, R., (2011). Analysis of the respiratory flow cycle morphology in chronic heart failure patients applying principal components analysis Engineering in Medicine and Biology Society 33rd Annual International Conference of the IEEE EMBS , IEEE (Boston, USA) Engineering in Medicine and Biology Society, 1725-1728

The study of flow cycle morphology provides new information about the breathing pattern. This study proposes the characterization of cycle morphology in chronic heart failure patients (CHF) patients, with periodic (PB) and non-periodic breathing (nPB) patterns, and healthy subjects. Principal component analysis is applied to extract a respiratory cycle model for each time segment defined by a 30-s moving window. To characterize morphology of the model waveform, a number of parameters are extracted whose significance is evaluated in terms of the following three classification problems: CHF patients with either PB or nPB, CHF patients versus healthy subjects, and nPB patients versus healthy subjects. 26 CHF patients (8 with PB and 18 with non-periodic breathing pattern (nPB)) and 35 healthy subjects are studied. The results show that a respiratory cycle compressed in time characterizes PB patients, i.e., shorter inspiratory and expiratory periods, and higher dispersion of the maximum inspiratory and expiratory flow value (accuracy of 87%). The maximal expiratory flow instant occurs earlier in CHF patients than in healthy subjects (accuracy of 87%), with a steeper slope between inspiration and expiration. It is also found that the standard deviation of the expiratory period, evaluated for each subject, is much lower in CHF patients than in healthy subjects. The maximal expiratory flow instant occurs earlier (accuracy of 84%) in nPB patients, when comparing subjects with similar respiratory pattern like nPB patients and healthy subjects.

Keywords: -----


Chaparro, J.A., Giraldo, B.F. , Caminal, P., Benito, S., (2011). Analysis of the respiratory pattern variability of patients in weaning process using autoregressive modeling techniques Engineering in Medicine and Biology Society 33rd Annual International Conference of the IEEE EMBS , IEEE (Boston, USA) Engineering in Medicine and Biology Society, 5690-5693

One of the most challenging problems in intensive care is the process of discontinuing mechanical ventilation, called weaning process. An unnecessary delay in the discontinuation process and an early weaning trial are undesirable. This paper proposes to analysis the respiratory pattern variability of these patients using autoregressive modeling techniques: autoregressive models (AR), autoregressive moving average models (ARMA), and autoregressive models with exogenous input (ARX). A total of 153 patients on weaning trials from mechanical ventilation were analyzed: 94 patients with successful weaning (group S); 38 patients that failed to maintain spontaneous breathing (group F), and 21 patients who had successful weaning trials, but required reintubation in less than 48 h (group R). The respiratory pattern was characterized by their time series. The results show that significant differences were obtained with parameters as model order and first coefficient of AR model, and final prediction error by ARMA model. An accuracy of 86% (84% sensitivity and 86% specificity) has been obtained when using order model and first coefficient of AR model, and mean of breathing duration.

Keywords: -----


Solà, J., Fiz, J.A., Morera, J., Jané, R., (2011). Bayes classification of snoring subjects with and without Sleep Apnea Hypopnea Syndrome, using a Kernel method Engineering in Medicine and Biology Society 33rd Annual International Conference of the IEEE EMBS , IEEE (Boston, USA) Engineering in Medicine and Biology Society, 6071-6074

The gold standard for diagnosing Sleep Apnea Hypopnea Syndrome (SAHS) is the Polysomnography (PSG), an expensive, labor-intensive and time-consuming procedure. It would be helpful to have a simple screening method that allowed to early determining the severity of a subject prior to his/her enrolment for a PSG. Several differences have been reported in the acoustic snoring characteristics between simple snorers and SAHS patients. Previous studies usually classify snoring subjects into two groups given a threshold of Apnea-Hypoapnea Index (AHI). Recently, Bayes multi-group classification with Gaussian Probability Density Function (PDF) has been proposed, using snore features in combination with apnea-related information. In this work we show that the Bayes classifier with Kernel PDF estimation outperforms the Gaussian approach and allows the classification of SAHS subjects according to their severity, using only the information obtained from snores. This could be the base of a single

Keywords: -----


Morgenstern, C., Schwaibold, M., Randerath, W., Bolz, A., Jané, R., (2011). Comparison of upper airway respiratory resistance measurements with the esophageal pressure/airflow relationship during sleep Engineering in Medicine and Biology Society 33rd Annual International Conference of the IEEE EMBS , IEEE (Boston, USA) Engineering in Medicine and Biology Society, 3205-3208

Measurement of upper airway resistance is of interest in sleep disordered breathing to estimate upper airway patency. Resistance is calculated with the airflow and respiratory effort signals. However, there is no consensus on a standard for upper airway resistance measurement. This study proposes a new benchmarking method to objectively compare different upper airway resistance measurement methods by objectively differentiating between breaths with inspiratory flow limitation (high resistance) and non-limited breaths (low resistance). Resistance was measured at peak-Pes, at peak-flow, at the linear portion of a polynomial equation, as an area comparative and as average resistance for an inspiration. A total of 20 patients with systematic, gold-standard esophageal pressure and nasal airflow acquisition were analyzed and 109,955 breaths were automatically extracted and evaluated. Relative resistance values in relationship to a reference resistance value obtained during wakefulness were also analyzed. The peak-Pes measurement method obtained the highest separation index with significant (p < 0.001) differences to the other methods, followed by the area comparative and the peak-flow methods. As expected, average resistances were significantly (p < 0.001) lower for the non-IFL than for the IFL group. Hence, we recommend employing the peak-Pes for accurate upper airway resistance estimation.

Keywords: -----


Sarlabous, L., Torres, A., Fiz, J.A., Gea, J., Martinez-Llorens, J.M., Morera, J., Jané, R. , (2011). Evaluation of the respiratory muscles efficiency during an incremental flow respiratory test Engineering in Medicine and Biology Society 33rd Annual International Conference of the IEEE EMBS , IEEE (Boston, USA) Engineering in Medicine and Biology Society, 3820-3823

The aim of this study was to evaluate the respiratory muscles efficiency during a progressive incremental flow (IF) respiratory test in healthy and Chronic Obstructive Pulmonary Disease (COPD) subjects. To achieve this, the relationship between mouth Inspiratory Pressure (IP) increment, which is a measure of the force produced by respiratory muscles, and respiratory muscular activity increment, evaluated by means of Mechanomyografic (MMG) signals of the diaphragm muscle, was analyzed. Moreover, the correlation between the respiratory efficiency measure and the obstruction severity of the subjects was also examined. Data from two groups of subjects were analyzed. One group consisted of four female subjects (two healthy subjects and two moderate COPD patients) and the other consisted of ten male subjects (six severe and four very severe COPD patients). All subjects performed an easy IF respiratory test, in which small IP values were reached. We have found that there is an increase of amplitude and a displacement towards low frequencies in the MMG signals when the IP increases. Furthermore, it has also been found that respiratory muscles efficiency is lower when greater the obstructive severity of the patients is, and it is lower in women than in men. These results suggest that the information provided by MMG signals could be used to evaluate the muscular efficiency in healthy and COPD subjects.

Keywords: -----


Mesquita, J., Fiz, J.A., Solà, J., Morera, J., Jané, R., (2011). Normal non-regular snores as a tool for screening SAHS severity Engineering in Medicine and Biology Society 33rd Annual International Conference of the IEEE EMBS , IEEE (Boston, USA) Engineering in Medicine and Biology Society, 3197-3200

Snoring is one of the earliest and most consistent sign of upper airway obstruction leading to Sleep Apnea-Hypopnea Syndrome (SAHS). Several studies on post-apneic snores, snores that are emitted immediately after an apnea, have already proven that this type of snoring is most distinct from that of normal snoring. However, post-apneic snores are more unlikely and sometimes even inexistent in simple snorers and mild SAHS subjects. In this work we address that issue by proposing the study of normal non-regular snores. They correspond to successive snores that are separated by normal breathing cycles. The results obtained establish the feasibility of acoustic parameters of normal non-regular snores as a promising tool for a prompt screening of SAHS severity.

Keywords: -----


Arcentales, A., Giraldo, B.F., Caminal, P., Benito, S., Voss, A., (2011). Recurrence quantification analysis of heart rate variability and respiratory flow series in patients on weaning trials Engineering in Medicine and Biology Society 33rd Annual International Conference of the IEEE EMBS , IEEE (Boston, USA) Engineering in Medicine and Biology Society, 2724-2727

Autonomic nervous system regulates the behavior of cardiac and respiratory systems. Its assessment during the ventilator weaning can provide information about physio-pathological imbalances. This work proposes a non linear analysis of the complexity of the heart rate variability (HRV) and breathing duration (TTot) applying recurrence plot (RP) and their interaction joint recurrence plot (JRP). A total of 131 patients on weaning trials from mechanical ventilation were analyzed: 92 patients with successful weaning (group S) and 39 patients that failed to maintain spontaneous breathing (group F). The results show that parameters as determinism (DET), average diagonal line length (L), and entropy (ENTR), are statistically significant with RP for TTot series, but not with HRV. When comparing the groups with JRP, all parameters have been relevant. In all cases, mean values of recurrence quantification analysis are higher in the group S than in the group F. The main differences between groups were found on the diagonal and vertical structures of the joint recurrence plot.

Keywords: -----


Jané, R., Fiz, J.A., Solà, J., Mesquita, J., Morera, J., (2011). Snoring analysis for the screening of sleep apnea hypopnea syndrome with a single-channel device developed using polysomnographic and snoring databases Engineering in Medicine and Biology Society 33rd Annual International Conference of the IEEE EMBS , IEEE (Boston, USA) Engineering in Medicine and Biology Society, 8331-8333

Several studies have shown differences in acoustic snoring characteristics between patients with Sleep Apnea-Hypopnea Syndrome (SAHS) and simple snorers. Usually a few manually isolated snores are analyzed, with an emphasis on postapneic snores in SAHS patients. Automatic analysis of snores can provide objective information over a longer period of sleep. Although some snore detection methods have recently been proposed, they have not yet been applied to full-night analysis devices for screening purposes. We used a new automatic snoring detection and analysis system to monitor snoring during full-night studies to assess whether the acoustic characteristics of snores differ in relation to the Apnea-Hypopnea Index (AHI) and to classify snoring subjects according to their AHI. A complete procedure for device development was designed, using databases with polysomnography (PSG) and snoring signals. This included annotation of many types of episodes by an expert physician: snores, inspiration and exhalation breath sounds, speech and noise artifacts, The AHI of each subject was estimated with classical PSG analysis, as a gold standard. The system was able to correctly classify 77% of subjects in 4 severity levels, based on snoring analysis and sound-based apnea detection. The sensitivity and specificity of the system, to identify healthy subjects from pathologic patients (mild to severe SAHS), were 83% and 100%, respectively. Besides, the Apnea Index (AI) obtained with the system correlated with the obtained by PSG or Respiratory Polygraphy (RP) (r=0.87, p<0.05).

Keywords: -----


Garde, A., Sörnmo, L., Jané, R., Giraldo, B., (2010). Breathing pattern characterization in chronic heart failure patients using the respiratory flow signal Annals of Biomedical Engineering 38, (12), 3572-3580

This study proposes a method for the characterization of respiratory patterns in chronic heart failure (CHF) patients with periodic breathing (PB) and nonperiodic breathing (nPB), using the flow signal. Autoregressive modeling of the envelope of the respiratory flow signal is the starting point for the pattern characterization. Spectral parameters extracted from the discriminant frequency band (DB) are used to characterize the respiratory patterns. For each classification problem, the most discriminant parameter subset is selected using the leave-one-out cross-validation technique. The power in the right DB provides an accuracy of 84.6% when classifying PB vs. nPB patterns in CHF patients, whereas the power of the DB provides an accuracy of 85.5% when classifying the whole group of CHF patients vs. healthy subjects, and 85.2% when classifying nPB patients vs. healthy subjects.

Keywords: Chronic heart failure, AR modeling, Respiratory pattern, Discriminant band, Periodic and nonperiodic breathing


Caminal, P., Giraldo, B. F., Vallverdu, M., Benito, S., Schroeder, R., Voss, A., (2010). Symbolic dynamic analysis of relations between cardiac and breathing cycles in patients on weaning trials Annals of Biomedical Engineering 38, (8), 2542-52

Traditional time-domain techniques of data analysis are often not sufficient to characterize the complex dynamics of the cardiorespiratory interdependencies during the weaning trials. In this paper, the interactions between the heart rate (HR) and the breathing rate (BR) were studied using joint symbolic dynamic analysis. A total of 133 patients on weaning trials from mechanical ventilation were analyzed: 94 patients with successful weaning (group S) and 39 patients that failed to maintain spontaneous breathing (group F). The word distribution matrix enabled a coarse-grained quantitative assessment of short-term nonlinear analysis of the cardiorespiratory interactions. The histogram of the occurrence probability of the cardiorespiratory words presented a higher homogeneity in group F than in group S, measured with a higher number of forbidden words in group S as well as a higher number of words whose probability of occurrence is higher than a probability threshold in group S. The discriminant analysis revealed the best results when applying symbolic dynamic variables. Therefore, we hypothesize that joint symbolic dynamic analysis provides enhanced information about different interactions between HR and BR, when comparing patients with successful weaning and patients that failed to maintain spontaneous breathing in the weaning procedure.

Keywords: Dynamical nonlinearities analysis, Cardiorespiratory interdependencies, Joint symbolic dynamic, Weaning procedure


Fiz, J. A., Jané, R., Solà, J., Abad, J., Garcia, M. A., Morera, J., (2010). Continuous analysis and monitoring of snores and their relationship to the apnea-hypopnea index Laryngoscope 120, (4), 854-862

Objectives/Hypothesis: We used a new automatic snoring detection and analysis system to monitor snoring during full-night polysomnography to assess whether the acoustic characteristics of snores differ in relation to the apnea-hypopnea index (AHI) and to classify subjects according to their AHI Study Design: Individual Case-Control Study. Methods: Thirty-seven snorers (12 females and 25 males, ages 40-65 years; body mass index (BMI), 29.65 +/- 4.7 kg/m(2)) participated Subjects were divided into three groups: G1 (AHI <5), G2 (AHI >= 5, <15) and G3 (AHI >= 15) Snore and breathing sounds were : recorded with a tracheal microphone throughout 6 hours of nighttime polysomnography The snoring episodes identified were automatically and continuously analyzed with a previously trained 2-layer feed-forward neural network. Snore number, average intensity, and power spectral density parameters were computed for every subject and compared among AHI groups. Subjects were classified using different AHI thresholds by means of a logistic regression model. Results: There were significant differences in supine position between G1 and G3 in sound intensity, number of snores; standard deviation of the spectrum, power ratio in bands 0-500, 100-500, and 0-800 Hz, and the symmetry coefficient (P < .03); Patients were classified with thresholds AHI = 5 and AHI = 15 with a sensitivity (specificity) of 87% (71%) and 80% (90%), respectively. Conclusions: A new system for automatic monitoring and analysis of snores during the night is presented. Sound intensity and several snore frequency parameters allow differentiation of snorers according to obstructive sleep apnea syndrome severity (OSAS). Automatic snore intensity and frequency monitoring and analysis could be a promising tool for screening OSAS patients, significantly improving the managing of this pathology.

Keywords: Breathing sounds, Signal interpretation, Sleep apnea syndromes, Snoring


Garde, A., Sörnmo, L., Jané, R., Giraldo, B. F., (2010). Correntropy-based spectral characterization of respiratory patterns in patients with chronic heart failure IEEE Transactions on Biomedical Engineering 57, (8), 1964-1972

A correntropy-based technique is proposed for the characterization and classification of respiratory flow signals in chronic heart failure (CHF) patients with periodic or nonperiodic breathing (PB or nPB, respectively) and healthy subjects. The correntropy is a recently introduced, generalized correlation measure whose properties lend themselves to the definition of a correntropy-based spectral density (CSD). Using this technique, both respiratory and modulation frequencies can be reliably detected at their original positions in the spectrum without prior demodulation of the flow signal. Single-parameter classification of respiratory patterns is investigated for three different parameters extracted from the respiratory and modulation frequency bands of the CSD, and one parameter defined by the correntropy mean. The results show that the ratio between the powers in the modulation and respiratory frequency bands provides the best result when classifying CHF patients with either PB or nPB, yielding an accuracy of 88.9%. The correntropy mean offers excellent performance when classifying CHF patients versus healthy subjects, yielding an accuracy of 95.2% and discriminating nPB patients from healthy subjects with an accuracy of 94.4%.

Keywords: Autoregressive (AR) modeling, Chronic heart failure (CHF), Correntropy spectral density (CSD), Linear classification, Periodic breathing (PB)


Morgenstern, C., Schwaibold, M., Randerath, W. J., Bolz, A., Jané, R., (2010). An invasive and a noninvasive approach for the automatic differentiation of obstructive and central hypopneas IEEE Transactions on Biomedical Engineering 57, (8), 1927-1936

The automatic differentiation of obstructive and central respiratory events is a major challenge in the diagnosis of sleep-disordered breathing. Esophageal pressure (Pes) measurement is the gold-standard method to identify these events. This study presents a new classifier that automatically differentiates obstructive and central hypopneas with the Pes signal and a new approach for an automatic noninvasive classifier with nasal airflow. An overall of 28 patients underwent night polysomnography with Pes recording, and a total of 769 hypopneas were manually scored by human experts to create a gold-standard annotation set. Features were automatically extracted from the Pes signal to train and test the classifiers (discriminant analysis, support vector machines, and adaboost). After a significantly (p < 0.01) higher incidence of inspiratory flow limitation episodes in obstructive hypopneas was objectively, invasively assessed compared to central hypopneas, the feasibility of an automatic noninvasive classifier with features extracted from the airflow signal was demonstrated. The automatic invasive classifier achieved a mean sensitivity, specificity, and accuracy of 0.90 after a 100-fold cross validation. The automatic noninvasive feasibility study obtained similar hypopnea differentiation results as a manual noninvasive classification algorithm. Hence, both systems seem promising for the automatic differentiation of obstructive and central hypopneas.

Keywords: Automatic differentiation, Central hypopnea, Esophageal pressure (Pes), Inspiratory flow limitation (IFL), Noninvasive classification, Obstructive hypopnea


Garde, A., Schroeder, R., Voss, A., Caminal, P., Benito, S., Giraldo, B., (2010). Patients on weaning trials classified with support vector machines Physiological Measurement 31, (7), 979-993

The process of discontinuing mechanical ventilation is called weaning and is one of the most challenging problems in intensive care. An unnecessary delay in the discontinuation process and an early weaning trial are undesirable. This study aims to characterize the respiratory pattern through features that permit the identification of patients' conditions in weaning trials. Three groups of patients have been considered: 94 patients with successful weaning trials, who could maintain spontaneous breathing after 48 h ( GSucc ); 39 patients who failed the weaning trial ( GFail ) and 21 patients who had successful weaning trials, but required reintubation in less than 48 h ( GRein ). Patients are characterized by their cardiorespiratory interactions, which are described by joint symbolic dynamics (JSD) applied to the cardiac interbeat and breath durations. The most discriminating features in the classification of the different groups of patients ( GSucc , GFail and GRein ) are identified by support vector machines (SVMs). The SVM-based feature selection algorithm has an accuracy of 81% in classifying GSucc versus the rest of the patients, 83% in classifying GRein versus GSucc patients and 81% in classifying GRein versus the rest of the patients. Moreover, a good balance between sensitivity and specificity is achieved in all classifications.

Keywords: Mechanical ventilation, Weaning, Support vector machines, Joint symbolic dynamics


Correa, R., Laciar, E., Arini, P., Jané, R., (2010). Analysis of QRS loop in the Vectorcardiogram of patients with Chagas' disease Engineering in Medicine and Biology Society (EMBC) 32nd Annual International Conference of the IEEE , IEEE (Buenos Aires, Argentina) , 2561-2564

In the present work, we have studied the QRS loop in the Vectorcardiogram (VCG) of 95 chronic chagasic patients classified in different groups (I, II and III) according to their degree of myocardial damage. For comparison, the VCGs of 11 healthy subjects used as control group (Group O) were also examined. The QRS loop was obtained for each patient from the XYZ orthogonal leads of their High-Resolution Electrocardiogram (HRECG) records. In order to analyze the variations of QRS loop in each detected beat, it has been proposed in this study the following vectorcardiographic parameters a) Maximum magnitude of the cardiac depolarization vector, b) Volume, c) Area of QRS loop, d) Ratio between the Area and Perimeter, e) Ratio between the major and minor axes of the QRS loop and f) QRS loop Energy. It has been found that one or more indexes exhibited statistical differences (p<0.05) between groups 0-II, O-III, I-II, I-III and II-III. We concluded that the proposed method could be use as complementary diagnosis technique to evaluate the degree of myocardial damage in chronic chagasic patients.

Keywords: Practical, Experimental/ bioelectric phenomena, Diseases, Electrocardiography, Medical signal, Processing/ QRS loop, Vectorcardiogram, Cardiac depolarization vector, Myocardial damage, Chagas disease, Complementary diagnosis technique, High-resolution electrocardiogram


Morgenstern, C., Schwaibold, M., Randerath, W., Bolz, A., Jané, R., (2010). Automatic non-invasive differentiation of obstructive and central hypopneas with nasal airflow compared to esophageal pressure Engineering in Medicine and Biology Society (EMBC) 32nd Annual International Conference of the IEEE , IEEE (Buenos Aires, Argentina) , 6142-6145

The differentiation of obstructive and central respiratory events is a major challenge in the diagnosis of sleep disordered breathing. Esophageal pressure (Pes) measurement is the gold-standard method to identify these events but its invasiveness deters its usage in clinical routine. Flattening patterns appear in the airflow signal during episodes of inspiratory flow limitation (IFL) and have been shown with invasive techniques to be useful to differentiate between central and obstructive hypopneas. In this study we present a new method for the automatic non-invasive differentiation of obstructive and central hypopneas solely with nasal airflow. An overall of 36 patients underwent full night polysomnography with systematic Pes recording and a total of 1069 hypopneas were manually scored by human experts to create a gold-standard annotation set. Features were automatically extracted from the nasal airflow signal to train and test our automatic classifier (Discriminant Analysis). Flattening patterns were non-invasively assessed in the airflow signal using spectral and time analysis. The automatic non-invasive classifier obtained a sensitivity of 0.71 and an accuracy of 0.69, similar to the results obtained with a manual non-invasive classification algorithm. Hence, flattening airflow patterns seem promising for the non-invasive differentiation of obstructive and central hypopneas.

Keywords: Practical, Experimental/ biomedical measurement, Feature extraction, Flow measurement, Medical disorders, Medical signal processing, Patient diagnosis, Pneumodynamics, Pressure measurement, Signal classification, Sleep, Spectral analysis/ automatic noninvasive differentiation, Obstructive hypopnea, Central hypopnea, Inspiratory flow limitation, Nasal airflow, Esophageal pressure, Polysomnography, Feature extraction, Discriminant analysis, Spectral analysis


Garde, A., Sörnmo, L., Jané, R., Giraldo, B. F., (2010). Correntropy-based nonlinearity test applied to patients with chronic heart failure Engineering in Medicine and Biology Society (EMBC) 32nd Annual International Conference of the IEEE , IEEE (Buenos Aires, Argentina) , 2399-2402

In this study we propose the correntropy function as a discriminative measure for detecting nonlinearities in the respiratory pattern of chronic heart failure (CHF) patients with periodic or nonperiodic breathing pattern (PB or nPB, respectively). The complexity seems to be reduced in CHF patients with higher risk level. Correntropy reflects information on both, statistical distribution and temporal structure of the underlying dataset. It is a suitable measure due to its capability to preserve nonlinear information. The null hypothesis considered is that the analyzed data is generated by a Gaussian linear stochastic process. Correntropy is used in a statistical test to reject the null hypothesis through surrogate data methods. Various parameters, derived from the correntropy and correntropy spectral density (CSD) to characterize the respiratory pattern, presented no significant differences when extracted from the iteratively refined amplitude adjusted Fourier transform (IAAFT) surrogate data. The ratio between the powers in the modulation and respiratory frequency bands R was significantly different in nPB patients, but not in PB patients, which reflects a higher presence of nonlinearities in nPB patients than in PB patients.

Keywords: Practical, Theoretical or Mathematical, Experimental/cardiology diseases, Fourier transforms, Medical signal processing, Pattern classification, Pneumodynamics, Spectral analysis, Statistical analysis, Stochastic processes/ correntropy based nonlinearity test, Chronic heart failure, Correntropy function, Respiratory pattern nonlinearities, CHF patients, Nonperiodic breathing pattern, Dataset statistical distribution, Dataset temporal structure, Nonlinear information, Null hypothesis, Gaussian linear stochastic process, Statistical test, Correntropy spectral density, Iteratively refined amplitude adjusted Fourier transform, Surrogate data, Periodic breathing pattern


Sarlabous, L., Torres, A., Fiz, J. A., Gea, J., Marti nez-Llorens, J. M., Morera, J., Jané, R., (2010). Interpretation of the approximate entropy using fixed tolerance values as a measure of amplitude variations in biomedical signals Engineering in Medicine and Biology Society (EMBC) 32nd Annual International Conference of the IEEE , IEEE (Buenos Aires, Argentina) , 5967-5970

A new method for the quantification of amplitude variations in biomedical signals through moving approximate entropy is presented. Unlike the usual method to calculate the approximate entropy (ApEn), in which the tolerance value (r) varies based on the standard deviation of each moving window, in this work ApEn has been computed using a fixed value of r. We called this method, moving approximate entropy with fixed tolerance values: ApEn/sub f/. The obtained results indicate that ApEn/sub f/ allows determining amplitude variations in biomedical data series. These amplitude variations are better determined when intermediate values of tolerance are used. The study performed in diaphragmatic mechanomyographic signals shows that the ApEn/sub f/ curve is more correlated with the respiratory effort than the standard RMS amplitude parameter. Furthermore, it has been observed that the ApEn/sub f/ parameter is less affected by the existence of impulsive, sinusoidal, constant and Gaussian noises in comparison with the RMS amplitude parameter.

Keywords: Practical, Theoretical or Mathematical/ biomechanics, Entropy, Gaussian noise, Medical signal processing, Muscle, Random processes/ approximate entropy interpretation, Fixed tolerance values, Diaphragmatic mechanomyographic signals, ApEnf curve, Respiratory effort, Gaussian noises


Correa, L. S., Laciar, E., Mut, V., Giraldo, B. F., Torres, A., (2010). Multi-parameter analysis of ECG and Respiratory Flow signals to identify success of patients on weaning trials Engineering in Medicine and Biology Society (EMBC) 32nd Annual International Conference of the IEEE , IEEE (Buenos Aires, Argentina) -----, 6070-6073

Statistical analysis, power spectral density, and Lempel Ziv complexity, are used in a multi-parameter approach to analyze four temporal series obtained from the Electrocardiographic and Respiratory Flow signals of 126 patients on weaning trials. In which, 88 patients belong to successful group (SG), and 38 patients belong to failure group (FG), i.e. failed to maintain spontaneous breathing during trial. It was found that mean values of cardiac inter-beat and breath durations give higher values for SG than for FG; Kurtosis coefficient of the spectrum of the rapid shallow breathing index is higher for FG; also Lempel Ziv complexity mean values associated with the respiratory flow signal are bigger for FG. Patients were then classified with a pattern recognition neural network, obtaining 80% of correct classifications (81.6% for FG and 79.5% for SG).

Keywords: Electrocardiography, Medical signal processing, Neural nets, Pattern recognition, Pneumodynamics, Signal classification, Statistical analysis, ECG, Kurtosis coefficient, Lempel Ziv complexity, Breath durations, Cardiac interbeat durations, Electrocardiography, Multiparameter analysis, Pattern recognition neural network, Power spectral density, Respiratory flow signals, Signal classification, Spontaneous breathing, Statistical analysis, Weaning trials


Leder, R. S., Schlotthauer, G., Penzel, T., Jané, R., (2010). The natural history of the sleep and respiratory engineering track at EMBC 1988 to 2010 Engineering in Medicine and Biology Society (EMBC) 32nd Annual International Conference of the IEEE , IEEE (Buenos Aires, Argentina) , 288-291

Sleep science and respiratory engineering as medical subspecialties and research areas grew up side-by-side with biomedical engineering. The formation of EMBS in the 1950's and the discovery of REM sleep in the 1950's led to parallel development and interaction of sleep and biomedical engineering in diagnostics and therapeutics.

Keywords: Practical/ biomedical equipment, Biomedical measurement, Patient diagnosis, Patient monitoring, Patient treatment, Pneumodynamics, Sleep/ sleep engineering, Respiratory engineering, Automatic sleep analysis, Automatic sleep interpretation systems, Breathing, Biomedical, Engineering, Diagnostics, Therapeutics, REM sleep, Portable, Measurement, Ambulatory measurement, Monitoring


Torres, A., Sarlabous, L., Fiz, j A., Gea, J., Marti nez-Llorens, J. M., Morera, J., Jané, R., (2010). Noninvasive measurement of inspiratory muscle performance by means of diaphragm muscle mechanomyographic signals in COPD patients during an incremental load respiratory test Engineering in Medicine and Biology Society (EMBC) 32nd Annual International Conference of the IEEE , IEEE (Buenos Aires, Argentina) , 2493-2496

The study of mechanomyographic (MMG) signals of respiratory muscles is a promising noninvasive technique in order to evaluate the respiratory muscular effort and efficiency. In this work, the MMG signal of the diaphragm muscle it is evaluated in order to assess the respiratory muscular function in Chronic Obstructive Pulmonary Disease (COPD) patients. The MMG signals from left and right hemidiaphragm were acquired using two capacitive accelerometers placed on both left and right sides of the costal wall surface. The MMG signals and the inspiratory pressure signal were acquired while the COPD patients carried out an inspiratory load respiratory test. The population of study is composed of a group of 6 patients with severe COPD (FEV1>50% ref and DLCO<50% ref). We have found high positive correlation coefficients between the maximum inspiratory pressure (IPmax) developed in a respiratory cycle and different amplitude parameters of both left and right MMG signals (RMS, left: 0.68+/-0.11 - right: 0.69+/-0.12; Re nyi entropy, left: - 0.73+/-0.10 - right: 0.77+/-0.08; Multistate Lempel-Ziv, left: 0.73+/-0.17 - right: 0.74+/-0.08), and negative correlation between the Pmax and the maximum frequency of the MMG signal spectrum (left: -0.39+/-0.19 - right: -0.65+/-0.09). Furthermore, we found that the slope of the evolution of the MMG amplitude parameters, as the load increases during the respiratory test, has positive correlation with the %FEV1/FVC pulmonary function test parameter of the six COPD patients analyzed (RMS, left: 0.38 - right: 0.41; Re nyi entropy, left: 0.45 - right: 0.63; Multistate Lempel-Ziv, left: 0.39 - right: 0.64). These results suggest that the information provided by MMG signals could be used in order to evaluate the respiratory effort and the muscular efficiency in COPD patients.

Keywords: Accelerometers, Biomechanics, Biomedical measurement, Diseases, Medical signal processing, Muscle


Mesquita, J., Fiz, J. A., Solà, J., Morera, J., Jané, R., (2010). Regular and non regular snore features as markers of SAHS Engineering in Medicine and Biology Society (EMBC) 32nd Annual International Conference of the IEEE , IEEE (Buenos Aires, Argentina) , 6138-6141

Sleep Apnea-Hypopnea Syndrome (SAHS) diagnosis is still done with an overnight multi-channel polysomnography. Several efforts are being made to study profoundly the snore mechanism and discover how it can provide an opportunity to diagnose the disease. This work introduces the concept of regular snores, defined as the ones produced in consecutive respiratory cycles, since they are produced in a regular way, without interruptions. We applied 2 thresholds (TH/sub adaptive/ and TH/sub median/) to the time interval between successive snores of 34 subjects in order to select regular snores from the whole all-night snore sequence. Afterwards, we studied the effectiveness that parameters, such as time interval between successive snores and the mean intensity of snores, have on distinguishing between different levels of SAHS severity (AHI (Apnea-Hypopnea Index)<5h/sup -1/, AHI<10 h/sup -1/, AHI<15h/sup -1/, AHI<30h/sup -1/). Results showed that TH/sub adaptive/ outperformed TH/sub median/ on selecting regular snores. Moreover, the outcome achieved with non-regular snores intensity features suggests that these carry key information on SAHS severity.

Keywords: Practical, Experimental/ acoustic signal processing, Bioacoustics, Biomedical measurement, Diseases, Feature extraction, Medical signal processing, Patient diagnosis, Pneumodynamics, Sleep/ nonregular snore features, SAHS markers, Sleep apnea hypopnea syndrome, Overnight multichannel polysomnography, Snore mechanism


Arcentales, A., Giraldo, B. F., Caminal, P., Diaz, I., Benito, S., (2010). Spectral analysis of the RR series and the respiratory flow signal on patients in weaning process Engineering in Medicine and Biology Society (EMBC) 32nd Annual International Conference of the IEEE , IEEE (Buenos Aires, Argentina) , 2485-2488

A considerable number of patients in weaning process have problems to keep spontaneous breathing during the trial and after it. This study proposes to extract characteristic parameters of the RR series and respiratory flow signal according to the patients' condition in weaning test. Three groups of patients have been considered: 93 patients with successful trials (group S), 40 patients that failed to maintain spontaneous breathing (group F), and 21 patients who had successful weaning trials, but that had to be reintubated before 48 hours (group R). The characterization was performed using spectral analysis of the signals, through the power spectral density, cross power spectral density and Coherence method. The parameters were extracted on the three frequency bands (VLF, LF and HF), and the principal statistical differences between groups were obtained in bands of VLF and HF. The results show an accuracy of 76.9% in the classification of the groups S and F.

Keywords: Biomedical measurement, Electrocardiography, Medical signal processing, Pneumodynamics, Spectral analysis, RR series, Coherence method, Cross power spectral density, Electrocardiography, Principal statistical differences, Respiratory flow signal, Spectral analysis, Spontaneous breathing, Weaning test


Estrada, L., Santamaria, J., Isetta, V., Iranzo, A., Navajas, D., Farre, R., (2010). Validation of an EEG-based algorithm for automatic detection of sleep onset in the multiple sleep latency test Proceedings of the World Congress on Engineering 2010 World Congress on Engineering 2010 , IAENG (International Association of Engineers) (London, UK) 1, 1-3

The Multiple Sleep Latency Test (MSLT) is a standard test to objectively evaluate patients with excessive daytime sleepiness. Sleep onset latencies are determined by visual analysis, which is costly and time-consuming. The aim of this study was to implement and test a single automatic algorithm to detect the sleep onset in the MSLT on the basis of electroencephalographic (EEG) signals. The designed algorithm computed the relative EEG spectral powers in the occipital area and detected the sleep onset corresponding to the intersection point between the lower and alpha frequencies. The algorithm performance was evaluated by comparing the sleep latencies computed automatically by the algorithm and by a sleep specialist using MSLT recordings from a total of 19 patients (95 naps). The mean difference in sleep latency between the two methods was 0.025 min and the limits of agreement were ± 2.46 min (Bland-Altman analysis). Moreover, the intra-class correlation coefficient showed a considerable inter-rater reliability (0.90). The algorithm accurately detected the sleep onset in the MSLT. The devised algorithm can be a useful tool to support and speed up the sleep specialist’s work in routine clinical MSLT assessment.

Keywords: Automatic Algorithm, Drowsiness, Electroencephalography, Multiple Sleep Latency Test, Polysomnography, Sleep onset


Fiz, J. A., Morera Prat, J., Jané, R., (2009). Treatment of patients with simple snoring Archivos de Bronconeumología 45, (10), 508-515

Management of snoring is part of the treatment offered to patients with obstructive sleep apnea syndrome. In patients who do not have this syndrome, however, snoring should be treated according to the severity of the condition. General or specific therapeutic measures should be applied to snorers that have concomitant cardiovascular disease or unrefreshing sleep and in cases in which an individual's snoring disturbs his/her partner's sleep. The present review examines the treatments currently available for snorers and the current state of knowledge regarding each option. It will also focus on the possible indications of these treatments and evaluate their effectiveness.

Keywords: Simple snoring, Treatment, General measures, Surgery


Morgenstern, C., Schwaibold, M., Randerath, W. J., Bolz, A., Jané, R., (2009). Assessment of changes in upper airway obstruction by automatic identification of inspiratory flow limitation during sleep IEEE Transactions on Biomedical Engineering 56, (8), 2006-2015

New techniques for automatic invasive and noninvasive identification of inspiratory flow limitation (IFL) are presented. Data were collected from 11 patients with full nocturnal polysomnography and gold-standard esophageal pressure (Pes) measurement. A total of 38,782 breaths were extracted and automatically analyzed. An exponential model is proposed to reproduce the relationship between Pes and airflow of an inspiration and achieve an objective assessment of changes in upper airway obstruction. The characterization performance of the model is appraised with three evaluation parameters: mean-squared error when estimating resistance at peak pressure, coefficient of determination, and assessment of IFL episodes. The model's results are compared to the two best-performing models in the literature. The obtained gold-standard IFL annotations were then employed to train, test, and validate a new noninvasive automatic IFL classification system. Discriminant analysis, support vector machines, and Adaboost algorithms were employed to objectively classify breaths noninvasively with features extracted from the time and frequency domains of the breaths' flowpatterns. The results indicated that the exponential model characterizes IFL and subtle relative changes in upper airway obstruction with the highest accuracy and objectivity. The new noninvasive automatic classification system also succeeded in identifying IFL episodes, achieving a sensitivity of 0.87 and a specificity of 0.85.

Keywords: Esophageal pressure, Exponential model, Inspiratory flow limitation, Noninvasive, Classification, Upper airway obstruction


Correa, R., Laciar, E., Arini, P., Jané, R., (2009). Analysis of QRS loop changes in the beat-to-beat vectocardiogram of ischemic patients undergoing PTCA Engineering in Medicine and Biology Society (EMBC) 31st Annual International Conference of the IEEE , IEEE (Minneapolis, USA) , 1750-1753

In the present work, we have studied dynamic changes of QRS loop in the Vectocardiogram (VCG) of 80 patients that underwent Percutaneous Transluminal Coronary Angioplasty (PTCA). The VCG was obtained for each patient using the XYZ orthogonal leads of their electrocardiographic (ECG) records acquired before, during and after PTCA procedure. In order to analyze the variations of VCG, it has been proposed in this study the following parameters a) Maximum module of the cardiac depolarization vector, b) Volume, c) and Area of vectocardiographic loop corresponding to the QRS complex of each beat, d) Maximum distance between Centroid and the Loop, e) Angle between the XY plane and the Optimum Plane, f) Relation between the Area and Perimeter. The results obtained indicate that the parameters proposed show significant statistics differences (p-value<0.05) before, during (with some exceptions at the first minute of balloon inflation) and after PTCA. We conclude that the variations observed in the proposed parameters correctly represent not only the morphological changes in the depolarization VCG but also they reflect the modifications in the levels of cardiac ischemia induced by PTCA.

Keywords: -----


Diez, P. F., Mut, V., Laciar, E., Torres, A., Avila, E., (2009). Application of the empirical mode decomposition to the extraction of features from EEG signals for mental task classification Engineering in Medicine and Biology Society (EMBC) 31st Annual International Conference of the IEEE , IEEE (Minneapolis, USA) , 2579-2582

In this work, it is proposed a technique for the feature extraction of electroencephalographic (EEG) signals for classification of mental tasks which is an important part in the development of Brain Computer Interfaces (BCI). The Empirical Mode Decomposition (EMD) is a method capable to process nonstationary and nonlinear signals as the EEG. This technique was applied in EEG signals of 7 subjects performing 5 mental tasks. For each mode obtained from the EMD and each EEG channel were computed six features: Root Mean Square (RMS), Variance, Shannon Entropy, Lempel-Ziv Complexity Value, and Central and Maximum Frequencies, obtaining a feature vector of 180 components. The Wilks' lambda parameter was applied for the selection of the most important variables reducing the dimensionality of the feature vector. The classification of mental tasks was performed using Linear Discriminate Analysis (LD) and Neural Networks (NN). With this method, the average classification over all subjects in database was 91±5% and 87±5% using LD and NN, respectively. It was concluded that the EMD allows getting better performances in the classification of mental tasks than the obtained with other traditional methods, like spectral analysis.

Keywords: -----


Morgenstern, C., Schwaibold, M., Randerath, W., Bolz, A., Jané, R., (2009). Automatic differentiation of obstructive and central hypopneas with esophageal pressure measurement during sleep Engineering in Medicine and Biology Society (EMBC) 31st Annual International Conference of the IEEE , IEEE (Minneapolis, USA) 2009, 7102-7105

The differentiation between obstructive and central respiratory events is one of the most recurrent tasks in the diagnosis of sleep disordered breathing. Esophageal pressure measurement is the gold-standard method to assess respiratory effort and identify these events. But as its invasiveness discourages its use in clinical routine, non-invasisve systems have been proposed for differentiation. However, their adoption has been slow due to their limited clinical validation, as the creation of manual, gold-standard validation sets by human experts is a cumbersome procedure. In this study, a new system is proposed for an objective automatic, gold-standard differentiation between obstructive and central hypopneas with the esophageal pressure signal. First, an overall of 356 hypopneas of 16 patients were manually scored by a human expert to create a gold-standard validation set. Then, features were extracted from each hypopnea to train and test classifiers (Discriminant Analysis, Support Vector Machines and adaboost classifiers) to differentiate between central and obstructive hypopneas with the gold-standard esophageal pressure signal. The automatic differentiation system achieved promising results, with a sensitivity of 0.88, a specificity of 0.93 and an accuracy of 0.90. Hence, this system seems promising for an automatic, gold-standard differentiation between obstructive and central hypopneas.

Keywords: -----


Garde, A., Sornmo, L., Jané, R., Giraldo, B. F., (2009). Correntropy-based analysis of respiratory patterns in patients with chronic heart failure Engineering in Medicine and Biology Society (EMBC) 31st Annual International Conference of the IEEE , IEEE (Minneapolis, USA) , 4687-4690

A correntropy-based technique is proposed for the analysis and characterization of respiratory flow signals in chronic heart failure (CHF) patients with both periodic and nonperiodic breathing (PB and nPB), and healthy subjects. Correntropy is a novel similarity measure which provides information on temporal structure and statistical distribution simultaneously. Its properties lend itself to the definition of the correntropy spectral density (CSD). An interesting result from CSD-based spectral analysis is that both the respiratory frequency and modulation frequency can be detected at their original positions in the spectrum without prior demodulation of the flow signal. The respiratory pattern is characterized by a number of spectral parameters extracted from the respiratory and modulation frequency bands. The results show that the power of the modulation frequency band offers excellent performance when classifying CHF patients versus healthy subjects, with an accuracy of 95.3%, and nPB patients versus healthy subjects with 90.7%. The ratio between the power in the modulation and respiration frequency bands provides the best results classifying CHF patients into PB and nPB, with an accuracy of 88.9%.

Keywords: -----


Arizmendi, C., Romero, E., Alquezar, R., Caminal, P., Díaz, I., Benito, S., Giraldo, B. F., (2009). Data mining of patients on weaning trials from mechanical ventilation using cluster analysis and neural networks Engineering in Medicine and Biology Society (EMBC) 31st Annual International Conference of the IEEE , IEEE (Minneapolis, USA) , 4343-4346

The process of weaning from mechanical ventilation is one of the challenges in intensive care. 149 patients under extubation process (T-tube test) were studied: 88 patients with successful trials (group S), 38 patients who failed to maintain spontaneous breathing and were reconnected (group F), and 23 patients with successful test but that had to be reintubated before 48 hours (group R). Each patient was characterized using 8 time series and 6 statistics extracted from respiratory and cardiac signals. A moving window statistical analysis was applied obtaining for each patient a sequence of patterns of 48 features. Applying a cluster analysis two groups with the majority dataset were obtained. Neural networks were applied to discriminate between patients from groups S, F and R. The best performance obtained was 84.0% of well classified patients using a linear perceptron trained with a feature selection procedure (that selected 19 of the 48 features) and taking as input the main cluster centroid. However, the classification baseline 69.8% could not be improved when using the original set of patterns instead of the centroids to classify the patients.


Orosco, L., Laciar, E., Correa, A. G., Torres, A., Graffigna, J. P., (2009). An epileptic seizures detection algorithm based on the empirical mode decomposition of EEG Engineering in Medicine and Biology Society (EMBC) 31st Annual International Conference of the IEEE , IEEE (Minneapolis, USA) , 2651-2654

Epilepsy is a neurological disorder that affects around 50 million people worldwide. The seizure detection is an important component in the diagnosis of epilepsy. In this study, the Empirical Mode Decomposition (EMD) method was proposed on the development of an automatic epileptic seizure detection algorithm. The algorithm first computes the Intrinsic Mode Functions (IMFs) of EEG records, then calculates the energy of each IMF and performs the detection based on an energy threshold and a minimum duration decision. The algorithm was tested in 9 invasive EEG records provided and validated by the Epilepsy Center of the University Hospital of Freiburg. In 90 segments analyzed (39 with epileptic seizures) the sensitivity and specificity obtained with the method were of 56.41% and 75.86% respectively. It could be concluded that EMD is a promissory method for epileptic seizure detection in EEG records.

Keywords: -----


Sarlabous, L., Torres, A., Fiz, J. A., Gea, J., Martinez-Llorens, J. M., Jané, R., (2009). Evaluation of the respiratory muscular function by means of diaphragmatic mechanomyographic signals in COPD patients Engineering in Medicine and Biology Society (EMBC) 31st Annual International Conference of the IEEE , IEEE (Minneapolis, USA) , 3925-3928

The study of mechanomyographic (MMG) signals of respiratory muscles is a promising technique in order to evaluate the respiratory muscular effort. In this work MMG signals from left and right hemidiaphragm (MMGl and MMGr, respectively) acquired during a respiratory protocol have been analyzed. The acquisition of both MMG signals was carried out by means of two capacitive accelerometers placed on both left and right sides of the costal wall. The signals were recorded in a group of six patients with Chronic Obstructive Pulmonary Disease (COPD). It has been observed that with the increase of inspiratory pressure it takes place an increase of the amplitude and a displacement toward low frequencies in both left and right MMG signals. Furthermore, it has been seen that the increase of amplitude and the decrease of frequency in MMG signals are more pronounced in severe COPD patients. This behaviour is similar for both MMGl and MMGr signals. Results suggest that the use of MMG signals could be potentially useful for the evaluation of the respiratory muscular function in COPD patients.

Keywords: -----


Sarlabous, L., Torres, A., Fiz, J. A., Gea, J., Galdiz, J. B., Jané, R., (2009). Multistate Lempel-Ziv (MLZ) index interpretation as a measure of amplitude and complexity changes Engineering in Medicine and Biology Society (EMBC) 31st Annual International Conference of the IEEE , IEEE (Minneapolis, USA) , 4375-4378

The Lempel-Ziv complexity (LZ) has been widely used to evaluate the randomness of finite sequences. In general, the LZ complexity has been used to determine the complexity grade present in biomedical signals. The LZ complexity is not able to discern between signals with different amplitude variations and similar random components. On the other hand, amplitude parameters, as the root mean square (RMS), are not able to discern between signals with similar power distributions and different random components. In this work, we present a novel method to quantify amplitude and complexity variations in biomedical signals by means of the computation of the LZ coefficient using more than two quantification states, and with thresholds fixed and independent of the dynamic range or standard deviation of the analyzed signal: the Multistate Lempel-Ziv (MLZ) index. Our results indicate that MLZ index with few quantification levels only evaluate the complexity changes of the signal, with high number of levels, the amplitude variations, and with an intermediate number of levels informs about both amplitude and complexity variations. The study performed in diaphragmatic mechanomyographic signals shows that the amplitude variations of this signal are more correlated with the respiratory effort than the complexity variations. Furthermore, it has been observed that the MLZ index with high number of levels practically is not affected by the existence of impulsive, sinusoidal, constant and Gaussian noises compared with the RMS amplitude parameter.

Keywords: -----


Correa, L. S., Laciar, E., Mut, V., Torres, A., Jané, R., (2009). Sleep apnea detection based on spectral analysis of three ECG - Derived respiratory signals Engineering in Medicine and Biology Society (EMBC) 31st Annual International Conference of the IEEE , IEEE (Minneapolis, USA) , 4723-4726

An apnea detection method based on spectral analysis was used to assess the performance of three ECG derived respiratory (EDR) signals. They were obtained on R wave area (EDR1), heart rate variability (EDR2) and R peak amplitude (EDR3) of ECG record in 8 patients with sleep apnea syndrome. The mean, central, peak and first quartile frequencies were computed from the spectrum every 1 min for each EDR. For each frequency parameter a threshold-based decision was carried out on every 1 min segment of the three EDR, classifying it as 'apnea' when its frequency value was below a determined threshold or as 'not apnea' in other cases. Results indicated that EDR1, based on R wave area has better performance in detecting apnea episodes with values of specificity (Sp) and sensitivity (Se) near 90%; EDR2 showed similar Sp but lower Se (78%); whereas EDR3 based on R peak amplitude did not detect appropriately the apneas episodes reaching Sp and Se values near 60%.

Keywords: -----


Correa, R., Arini, P. D., Laciar, E., Laguna, P., Jané, R., (2009). Study of morphological parameters of QRS loop using singular value decomposition during ischemia induced by coronary angioplasty 36th Annual Computers in Cardiology Conference (CinC) 36th Annual Computers in Cardiology Conference (CinC) , IEEE (Park City, USA) 36, 693-696

In this work we studied dynamic changes of ventricular depolarization loop evolution based on the Singular Value Decomposition (SVD) technique of 80 patients that underwent Percutaneous Transluminal Coronary Angioplasty (PTCA). The 8 independent ECG leads are subjected to SVD technique and are used to construct a new representation of QRS-SVD loops. In order to analyze the variations of QRS-SVD loops before, during and after PTCA, we proposed the following parameters: Maximum Module of the Depolarization Vector, Planar Area, Maximum Distance between Centroid and the Loop, Angle between the S1S2 plane and the Optimum Plane and Ratio between the Area and Perimeter. The results indicated that the parameters proposed show significant statistics differences during and after PTCA procedure vs. control. We concluded that the variations in the QRS-SVD loop before, during and after PTCA at ventricular depolarization can be described correctly through the proposed parameters.

Keywords: -----


Garde, A., Giraldo, B. F., Jané, R., Sornmo, L., (2009). Time-varying respiratory pattern characterization in chronic heart failure patients and healthy subjects Engineering in Medicine and Biology Society (EMBC) 31st Annual International Conference of the IEEE , IEEE (Minneapolis, USA) , 4007-4010

Patients with chronic heart failure (CHF) with periodic breathing (PB) and Cheyne-Stokes respiration (CSR) tend to exhibit higher mortality and poor prognosis. This study proposes the characterization of respiratory patterns in CHF patients and healthy subjects using the envelope of the respiratory flow signal, and autoregressive (AR) time-frequency analysis. In time-varying respiratory patterns, the statistical distribution of the AR coefficients, pole locations, and the spectral parameters that characterize the discriminant band are evaluated to identify typical breathing patterns. In order to evaluate the accuracy of this characterization, a feature selection process followed by linear discriminant analysis is applied. 26 CHF patients (8 patients with PB pattern and 18 with non-periodic breathing pattern (nPB)) are studied. The results show an accuracy of 83.9% with the mean of the main pole magnitude and the mean of the total power, when classifying CHF patients versus healthy subjects, and 83.3% for nPB versus healthy subjects. The best result when classifying CHF patients into PB and nPB was an accuracy of 88.9%, using the coefficient of variation of the first AR coefficient and the mean of the total power.

Keywords: -----


Seeck, A., Garde, A., Schuepbach, M., Giraldo, B., Sanz, E., Huebner, T., Caminal, P., Voss, A., (2009). Diagnosis of ischemic heart disease with cardiogoniometry - linear discriminant analysis versus support vector machines IFMBE Proceedings 4th European Conference of the International Federation for Medical and Biological Engineering (ed. Vander Sloten, Jos, Verdonck, Pascal, Nyssen, Marc, Haueisen, Jens), Springer Berlin Heidelberg (Berlin, Germany) 22, 389-392

The Ischemic Heart Disease (IHD) is characterized by an insufficient supply with blood of the myocardium usually caused by an artherosclerotic disease of the coronary arteries (coronary artery disease CAD). The IHD and its consequences have become a leading problem in the industrialized nations. The aim of this study was to evaluate a new diagnosing method, the cardiogoniometry, using two different classifying techniques: the method of linear discriminant function analysis (LDA) and the method of Support Vector Machines (SVM). Data of a group of 109 female subjects (62 healthy, 47 with IHD) were analyzed on the basis of extracted parameters from the three-dimensional vector loops of the heart. The LDA achieved an accuracy of 83,5% (Sensitivity 78,7%, Specificity 87,1%), whereas the SVM achieved an accuracy of 86% (Sensitivity 80,5%, Specificity 89,8%). It could be shown that cardiogoniometry, an electrophysiological diagnostic method performed at rest, detects variables that are helpful in identifying ischemic heart disease. As it is easy to apply, non-invasive, and provides an automated interpretation it may become an inexpensive addition to the cardiologic diagnostic armamentarium, possibly useful for early diagnosis of IHD or CAD, as well as in patients who do not tolerate exercise testing. It was also proven that by applying Support Vector Machines an increased diagnostic precision in comparison to the conventional discriminant function analysis can be achieved.

Keywords: Cardiogoniometry, Support Vector Machines, Nonlinear classifier, Linear discriminant analysis, Vector loop


Morgenstern, C., Jané, R., Schwaibold, M., Randerath, W., (2008). Automatic classification of inspiratory flow limitation assessed non-invasively during sleep IEEE Engineering in Medicine and Biology Society Conference Proceedings 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (ed. IEEE), IEEE (Vancouver, Canada) 1-8, 1132-1135

Detection of inspiratory flow limitation (IFL) is being recognized of increasing importance in order to diagnose pathologies related to sleep disordered breathing. Currently, IFL is usually identified with the help of invasive esophageal pressure measurement, still considered the gold-standard reference to assess respiratory effort. But the invasiveness of esophageal pressure measurement and its impact on sleep discourages its use in clinical routine. In this study, a new non-invasive automatic system is proposed for objective IFL classification. First, an automatic annotation system for IFL based on pressure/flow relationship was developed. Then, classifiers (Support Vector Machines and adaboost classifiers) were trained with these gold-standard references in order to objectively classify breaths non-invasively, solely based on the breaths' flow contours. The new non-invasive automatic classification system seems to be promising, as it achieved a sensitivity of 0.92 and a specificity of 0.89, outperforming prior classification results obtained by human experts.

Keywords: Upper airway-resistance


Morgenstern, C., Jané, R., Schwaibold, M., Randerath, W., (2008). Characterization of inspiratory flow limitation during sleep with an exponential model IEEE Engineering in Medicine and Biology Society Conference Proceedings 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (ed. IEEE), IEEE (Vancouver, Canada) 1-8, 2439-2442

Assessing incidence and severity of inspiratory flow limitation (TFL) is of importance for patients suffering of sleep disordered breathing (SDB) in order to diagnose a spectrum of different pathologies. In this study a new exponential equation is proposed to characterize the pressure/flow relationship of IFL and non-TFL breaths. Classical and alternative criteria are applied on the model's predictions in order to assess TFL, and its outcome is compared to the outcome of other models. The newly proposed exponential model seems to be promising, as it outperforms other models by achieving a global average sensitivity of 93% and specificity of 91%, and the lowest mean square error when estimating resistance at peak pressure. Additional statistical tests were performed on the exponential model's coefficients in order to determine if a coefficient based classification is possible.

Keywords: Resistance


Garde, A., Giraldo, B. F., Jané, R., Diaz, I., Herrera, S., Benito, S., Domingo, M., Bayes-Genis, A., (2008). Characterization of periodic and non-periodic breathing pattern in chronic heart failure patients IEEE Engineering in Medicine and Biology Society Conference Proceedings 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (ed. IEEE), IEEE (Vancouver, Canada) 1-8, 3227-3230

Periodic breathing (PB) has a high prevalence in chronic heart failure (CHF) patients with mild to moderate symptoms and poor ventricular function. This work proposes the analysis and characterization of the respiratory pattern to identify periodic breathing pattern (PB) and non-periodic breathing pattern (nPB) through the respiratory flow signal. The respiratory pattern analysis is based on the extraction and the study of the flow envelope signal. The flow envelope signal is modelled by an autoregressive model (AR) whose coefficients would characterize the respiratory pattern of each group. The goodness of the characterization is evaluated through a linear and non linear classifier applied to the AR coefficients. An adaptive feature selection is used before the linear and non linear classification, employing leave-one-out cross validation technique. With linear classification the percentage of well classified patients (8 PB and 18 nPB patients) is 84.6% using the statistically significant coefficients whereas with non linear classification, the percentage of well classified patients increase to more than 92% applying the best subset of coefficients extracted by a forward selection algorithm.

Keywords: Clinical-implications, Sleep


Diez, Pablo F., Laciar, Eric, Mut, Vicente, Avila, Enrique, Torres, Abel, (2008). A comparative study of the performance of different spectral estimation methods for classification of mental tasks IEEE Engineering in Medicine and Biology Society Conference Proceedings 30th Annual International Conference of the Ieee Engineering in Medicine and Biology Society (ed. IEEE), IEEE (Vancouver, Canada) 1-8, 1155-1158

In this paper we compare three different spectral estimation techniques for the classification of mental tasks. These techniques are the standard periodogram, the Welch periodogram and the Burg method, applied to electroencephalographic (EEG) signals. For each one of these methods we compute two parameters: the mean power and the root mean square (RMS), in various frequency bands. The classification of the mental tasks was conducted with a linear discriminate analysis. The Welch periodogram and the Burg method performed better than the standard periodogram. The use of the RMS allows better classification accuracy than the obtained with the power of EEG signals.

Keywords: Adult, Algorithms, Artificial Intelligence, Cognition, Electroencephalography, Female, Humans, Male, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, Task Performance and Analysis, User-Computer Interface


Solà, J., Jané, R., Fiz, J. A., Morera, J., (2008). Formant frequencies of normal breath sounds of snorers may indicate the risk of obstructive sleep apnea syndrome IEEE Engineering in Medicine and Biology Society Conference Proceedings 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (ed. IEEE), IEEE (Vancouver, Canada) 1-8, 3500-3503

Several differences between the airway of normal subjects and those with OSAS are well known. The characteristics of the upper airway may be indirectly studied through the formant frequencies of breathing sounds. In this work we analyze the formants of inspiration and exhalation sounds in snoring subjects with and without OSAS. Formant frequencies of inspiration and exhalation appear in the same bands as snores. Formant F1 is significantly lower in inspiration episodes of OSAS patients (p=0.008) with a decreasing tendency as the AHI increases (r=0.705). In addition, this formant has a significantly higher variability SF1 in pathological subjects, for both inspiration (p=0.022) and exhalation (p=0.038) episodes, as was previously found in snores. A higher variability of formant frequencies seems to be an indicator of the presence of OSAS. The proposed technique could allow the identification of OSAS patients from normal breathing alone.

Keywords: Upper airway


Correa, L. S., Laciar, E., Torres, A., Jané, R., (2008). Performance evaluation of three methods for respiratory signal estimation from the electrocardiogram IEEE Engineering in Medicine and Biology Society Conference Proceedings 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (ed. IEEE), IEEE (Vancouver, Canada) 1-8, 4760-4763

A comparative study of three methods for estimating respiratory signal through electrocardiogram (ECG) was carried out. The three methods analyzed were based on R wave area, R peak amplitude and heart rate variability (HRV). For each method, cross-correlation coefficient and spectral coherence in a range of frequencies up to 0.5 Hz were computed between the ECG derived respiratory signals (EDR) and the three real respiratory signals: oronasal, and two inductance plethysmographies recordings (chest and abdominal). Results indicate that EDR methods based on R wave area and HRV are better correlated and show a wider spectral coherence with real respiratory signals than the other EDR method based on R peak amplitude.

Keywords: Obstructive sleep-apnea


Torres, A., Fiz, J. A., Jané, R., Laciar, E., Galdiz, J. B., Gea, J., Morera, J., (2008). Renyi entropy and Lempel-Ziv complexity of mechanomyographic recordings of diaphragm muscle as indexes of respiratory effort IEEE Engineering in Medicine and Biology Society Conference Proceedings 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (ed. IEEE), IEEE (Vancouver, Canada) 1-8, 2112-2115

The study of the mechanomyographic (MMG) signals of respiratory muscles is a promising technique in order to evaluate the respiratory muscles effort. A new approach for quantifying the relationship between respiratory MMG signals and respiratory effort is presented by analyzing the spatiotemporal patterns in the MMG signal using two non-linear methods: Renyi entropy and Lempel-Ziv (LZ) complexity analysis. Both methods are well suited to the analysis of non-stationary biomedical signals of short length. In this study, MMG signals of the diaphragm muscle acquired by means of a capacitive accelerometer applied on the costal wall were analyzed. The method was tested on an animal model (dogs), and the diaphragmatic MMG signal was recorded continuously while two non anesthetized mongrel dogs performed a spontaneous ventilation protocol with an incremental inspiratory load. The performance in discriminating high and low respiratory effort levels with these two methods was analyzed with the evaluation of the Pearson correlation coefficient between the MMG parameters and respiratory effort parameters extracted from the inspiratory pressure signal. The results obtained show an increase of the MMG signal Renyi entropy and LZ complexity values with the increase of the respiratory effort. Compared with other parameters analyzed in previous works, both Renyi entropy and LZ complexity indexes demonstrates better performance in all the signals analyzed. Our results suggest that these non-linear techniques are useful to detect and quantify changes in the respiratory effort by analyzing MMG respiratory signals.

Keywords: Sound, Force


Orini, Michele, Giraldo, Beatriz F., Bailon, Raquel, Vallverdu, Montserrat, Mainardi, Luca, Benito, Salvador, Diaz, Ivan, Caminal, Pere, (2008). Time-frequency analysis of cardiac and respiratory parameters for the prediction of ventilator weaning IEEE Engineering in Medicine and Biology Society Conference Proceedings 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (ed. IEEE), IEEE (Vancouver, Canada) 1-8, 2793-2796

Mechanical ventilators are used to provide life support in patients with respiratory failure. Assessing autonomic control during the ventilator weaning provides information about physiopathological imbalances. Autonomic parameters can be derived and used to predict success in discontinuing from the mechanical support. Time-frequency analysis is used to derive cardiac and respiratory parameters, as well as their evolution in time, during ventilator weaning in 130 patients. Statistically significant differences have been observed in autonomic parameters between patients who are considered ready for spontaneous breathing and patients who are not. A classification based on respiratory frequency, heart rate and heart rate variability spectral components has been proposed and has been able to correctly classify more than 80% of the cases.

Keywords: Automatic Data Processing, Databases, Factual, Electrocardiography, Humans, Models, Statistical, Respiration, Respiration, Artificial, Respiratory Insufficiency, Respiratory Mechanics, Respiratory Muscles, Signal Processing, Computer-Assisted, Time Factors, Ventilator Weaning, Ventilators, Mechanical, Work of Breathing


Equipment

  • Research laboratory with full equipment for acquisition and processing of biomedical signal to test new sensors and to define clinical protocols (preliminary tests and control subjects)
  • Non-invasive Vital Signs Monitor for small lab animals (mice and rats) (Mouse-Ox Plus)
  • BIOPAC system for multichannel cardiac and respiratory biomedical signal acquisition
  • Databases of biomedical signals from hospitals and animal laboratories
  • Snoring analyzer equipment (SNORYZER)
  • Sensors, electrodes and microphones to obtain cardiac, respiratory, neural, muscular and sleep biomedical signals
  • Polisomnographic equipment available in the Sleep Laboratory of collaborator hospital
  • Beat to beat arterial blood pressure and haemodynamic monitor equipment
  • Computing server for high performance biomedical signals
  • Threshold™ IMT (Inspiratory Muscle Trainner) for respiratory muscle training (Phillips™)
  • Robust wearable wireless sensor device Shimmer3 (Shimmer Research Ltd., Dublin, Ireland).

Collaborations

  • Dr. J. Mark Ansermino
    Department of Anesthesiology, Pharmacology and Therapeutics, University of British Columbia, Vancouver, Canada
  • Prof. Antonio Bayes Genis
    Grup ICREC, Servei Cardiología Hospital Universitari Germans Trias i Pujol, Barcelona
  • Dr. Salvador Benito
    Hospital de la Santa Creu i Sant Pau, Barcelona
  • Prof. Dr. Konrad Bloch
    Pulmonary Division, University of Zurich, Switzerland
  • Prof. Armin Bolz
    Institute of Biomedical Engineering, University of Karlsruhe, Germany
  • Prof. Manuel Doblaré
    Grupo de Mecánica Estructural y Modelado de Materiales, Universidad de Zaragoza, Spain
  • Prof. Guy Dumont
    Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada
  • Prof. Ramon Farré
    Unitat de Biofísica i Bioenginyeria, Facultat de Medicina, Barcelona
  • Dr. Javier García-Casado
    Instituto Interuniversitario de Investigación en Bioingeniería y Tecnología Orientada al Ser Humano, Universidad Politécnica de Valencia
  • Dr. Joaquim Gea
    Servei Pneumologia, Hospital del Mar-IMIM, Barcelona
  • Dr. Alfredo Hernández
    Laboratoire Trataiment du Signal et de l’Image, Université de Rennes 1, Instituto Francés de Salud (INSERM), France
  • Dr. Eric Laciar
    Departamento de Electrónica y Automática, Universidad Nacional de San Juan, Argentina
  • Prof. Pablo Laguna
    Instituto de Investigación de Aragón (I3A), Universidad de Zaragoza, Spain
  • Dr. Barry Mersky
    Audiodontics, LLC, Bethesda, Maryland, USA
  • Prof. Dr. Thomas Penzel
    Interdisciplinary Sleep Center, Charité University Hospital, Berlin, Germany
  • Dr. Josep Morera Prat
    Servicio de Neumología, Hospital Germans Trias i Pujol, Badalona, Spain
  • Prof. Winfried J. Randerath
    Institut für Pneumologie, Klinik Bethanien, Solingen, Germany
  • Dr. Juan Ruiz
    Servei de Pneumología de l’Hospital Germans Trias i Pujol de Badalona
  • Dr. Matthias Schwaibold
    MCC-Med GmbH & Co. KG, Karlsruhe, Germany
  • Prof. Dr. Lotfi Senhadji
    Laboratoire Traitement du Signal et de l’Image (LTSI), Université de Rennes 1, Institut National de la Santé et de la Recherche Médicale (INSERM), France
  • Prof. Leif Sörnmo
    Signal processing group, Lund University, Sweden
  • Prof. Dr. Jaume Veciana
    Grupo de Nanociencia Molecular y Materiales Orgánicos del Instituto de Ciencia de Materiales de Barcelona (NANOMOL-CSIC), Barcelona
  • Prof. Andreas Voss
    University of Applied Sciences, Jena, Germany
  • Dr. Pierluigi Casale
    Laboratory for advanced research in microelectronics (IMEC), Eindhoven, The Netherlands
  • Dr. Francky Catthoor
    Laboratory for advanced research in microelectronics (IMEC), Leuven, Belgium
  • Dr. Miquel Domenech
    Dep. of Social Psychology, Universitat Autònoma de Barcelona
  • Dr. Caroline Jolley / Dr. John Moxham
    King’s College London, UK

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