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by Keyword: Sensitivity


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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


Karpas, Z., Guamán, A. V., Pardo, A., Marco, S., (2013). Comparison of the performance of three ion mobility spectrometers for measurement of biogenic amines Analytica Chimica Acta 758, (3), 122-129

The performance of three different types of ion mobility spectrometer (IMS) devices: GDA2 with a radioactive ion source (Airsense, Germany), UV-IMS with a photo-ionization source (G.A.S. Germany) and VG-Test with a corona discharge source (3QBD, Israel) was studied. The gas-phase ion chemistry in the IMS devices affected the species formed and their measured reduced mobility values. The sensitivity and limit of detection for trimethylamine (TMA), putrescine and cadaverine were compared by continuous monitoring of a stream of air with a given concentration of the analyte and by measurement of headspace vapors of TMA in a sealed vial. Preprocessing of the mobility spectra and the effectiveness of multivariate curve resolution techniques (MCR-LASSO) improved the accuracy of the measurements by correcting baseline effects and adjusting for variations in drift time as well as enhancing the signal to noise ratio and deconvolution of the complex data matrix to their pure components. The limit of detection for measurement of the biogenic amines by the three IMS devices was between 0.1 and 1.2 ppm (for TMA with the VG-Test and GDA, respectively) and between 0.2 and 0.7 ppm for putrescine and cadaverine with all three devices. Considering the uncertainty in the LOD determination there is almost no statistically significant difference between the three devices although they differ in their operating temperature, ionization method, drift tube design and dopant chemistry. This finding may have general implications on the achievable performance of classic IMS devices.

Keywords: Biogenic amines, Comparison of performance, Ion mobility spectrometry, Sensitivity, Signal processing, Vapor concentration


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


Dries, Koen, Helden, Suzanne, Riet, Joostte, Diez-Ahedo, Ruth, Manzo, Carlo, Oud, Machteld, Leeuwen, Frank, Brock, Roland, Garcia-Parajo, Maria, Cambi, Alessandra, Figdor, CarlG, (2012). Geometry sensing by dendritic cells dictates spatial organization and PGE2-induced dissolution of podosomes Cellular and Molecular Life Sciences 69, (11), 1889-1901

Assembly and disassembly of adhesion structures such as focal adhesions (FAs) and podosomes regulate cell adhesion and differentiation. On antigen-presenting dendritic cells (DCs), acquisition of a migratory and immunostimulatory phenotype depends on podosome dissolution by prostaglandin E2 (PGE2). Whereas the effects of physico-chemical and topographical cues have been extensively studied on FAs, little is known about how podosomes respond to these signals. Here, we show that, unlike for FAs, podosome formation is not controlled by substrate physico-chemical properties. We demonstrate that cell adhesion is the only prerequisite for podosome formation and that substrate availability dictates podosome density. Interestingly, we show that DCs sense 3-dimensional (3-D) geometry by aligning podosomes along the edges of 3-D micropatterned surfaces. Finally, whereas on a 2-dimensional (2-D) surface PGE2 causes a rapid increase in activated RhoA levels leading to fast podosome dissolution, 3-D geometric cues prevent PGE2-mediated RhoA activation resulting in impaired podosome dissolution even after prolonged stimulation. Our findings indicate that 2-D and 3-D geometric cues control the spatial organization of podosomes. More importantly, our studies demonstrate the importance of substrate dimensionality in regulating podosome dissolution and suggest that substrate dimensionality plays an important role in controlling DC activation, a key process in initiating immune responses.

Keywords: Mechanosensitivity, Podosomes, Dendritic cell, Adhesion


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


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


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