Staff member


Beatriz Giraldo Giraldo

Senior Researcher
Biomedical Signal Processing and Interpretation
bgiraldo@ibecbarcelona.eu
+34 934 020 503
Staff member publications

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


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.


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


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.


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


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


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.


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


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


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.


Jané, R., Caminal, P., Giraldo, B., Solà, J., Torres, A., (2014). Libro de Actas del CASEIB 2014 XXXII Congreso Anual de la SEIB , CASEIB-IBEC (Barcelona, Spain) , 20

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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


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


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


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


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


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.


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


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: -----


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: -----


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


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)


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


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


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


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


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.


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


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


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


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