by Keyword: Events
Romero, D, Jané, R, (2023). Dynamic Bayesian Model for Detecting Obstructive Respiratory Events by Using an Experimental Model Sensors 23, 3371-3371 
In this study, we propose a model-based tool for the detection of obstructive apnea episodes by using ECG features from a single lead channel. Several sequences of recurrent apnea were provoked in separate 15-min periods in anesthetized rats during an experimental model of obstructive sleep apnea (OSA). Morphology-based ECG markers and the beat-to-beat interval (RR) were assessed in each sequence. These markers were used to train dynamic Bayesian networks (DBN) with different orders and feature combinations to find a good tradeoff between network complexity and apnea-detection performance. By using a filtering approach, the resulting DBNs were used to infer the apnea probability signal for subsequent episodes in the same rat. These signals were then processed using by 15-s epochs to determine whether epochs were classified as apneic or nonapneic. Our results showed that fifth-order models provided suitable RMSE values, since higher order models become significantly more complex and present worse generalization. A global threshold of 0.2 gave the best overall performance for all combinations tested, with Acc = 81.3%, Se = 69.8% and Sp = 81.5%, using only two parameters including the RR and Ds (R-wave downslope) markers. We concluded that multivariate models using DBNs represent a powerful tool for detecting obstructive apnea episodes in short segments, which may also serve to estimate the number of total events in a given time period.
JTD Keywords: chronic respiratory diseases, obstructive sleep apnea, probabilistic models, Obstructive sleep apnea,probabilistic models,respiratory events,chronic respiratory disease, Respiratory events, Sleep-apnea syndrome,automated detection,oxygen-saturation,classification,recordings,signal
Romero, D, Calvo, M, Le Rolle, V, Behar, N, Mabo, P, Hernandez, A, (2022). Multivariate ensemble classification for the prediction of symptoms in patients with Brugada syndrome Medical & Biological Engineering & Computing 60, 81-94
Identification of asymptomatic patients at higher risk for suffering cardiac events remains controversial and challenging in Brugada syndrome (BS). In this work, we proposed an ECG-based classifier to predict BS-related symptoms, by merging the most predictive electrophysiological features derived from the ventricular depolarization and repolarization periods, along with autonomic-related markers. The initial feature space included local and dynamic ECG markers, assessed during a physical exercise test performed in 110 BS patients (25 symptomatic). Morphological, temporal and spatial properties quantifying the ECG dynamic response to exercise and recovery were considered. Our model was obtained by proposing a two-stage feature selection process that combined a resampled-based regularization approach with a wrapper model assessment for balancing, simplicity and performance. For the classification step, an ensemble was constructed by several logistic regression base classifiers, whose outputs were fused using a performance-based weighted average. The most relevant predictors corresponded to the repolarization interval, followed by two autonomic markers and two other makers of depolarization dynamics. Our classifier allowed for the identification of novel symptom-related markers from autonomic and dynamic ECG responses during exercise testing, suggesting the need for multifactorial risk stratification approaches in order to predict future cardiac events in asymptomatic BS patients.
JTD Keywords: brugada syndrome, depolarization disorders, ensemble classifier, heart-rate recovery, Acute myocardial-ischemia, Autonomics, Brugada syndrome, Brugadum syndrome, Cardiac death, Depolarization, Depolarization disorder, Depolarization disorders, Dynamic ecg, Electrocardiography, Electrophysiology, Ensemble classifier, Ensemble-classifier, Events, Exercise, Forecasting, Heart, Heart-rate, Heart-rate recovery, Prognosis, Qrs, Quantification, Recovery, Repolarization, Sudden cardiac death
Rodriguez, Segui, Bucior, I., Burger, M. M., Samitier, J., Errachid, A., Fernàndez-Busquets, X., (2007). Application of a bio-QCM to study carbohydrates self-interaction in presence of calcium Transducers '07 & Eurosensors Xxi, Digest of Technical Papers 
14th International Conference on Solid-State Sensors, Actuators and Microsystems , IEEE (Lyon, France) 1-2, 1995-1998
In the past years, the quartz crystal microbalance (QCM) has been successfully applied to follow interfacial physical chemistry phenomena in a label free and real time manner. However, carbohydrate self adhesion has only been addressed partially using this technique. Carbohydrates play an important role in cell adhesion, providing a highly versatile form of attachment, suitable for biologically relevant recognition events in the initial steps of adhesion. Here, we provide a QCM study of carbohydrates' self-recognition in the presence of calcium, based on a species-specific cell recognition model provided by marine sponges. Our results show a difference in adhesion kinetics when varying either the calcium concentration (with a constant carbohydrate concentration) or the carbohydrate concentration (with constant calcium concentration).
JTD Keywords: Biomedical materials, Calcium, Cellular biophysics, Microbalances, Porous materials, Quartz, Surface chemistry/ bio-QCM, Carbohydrates self-interaction, Quartz crystal microbalance, Interfacial physical chemistry phenomena, Carbohydrate self adhesion, Biologically relevant recognition events, Marine sponges, Adhesion kinetics, Calcium concentration, Carbohydrate concentration, Biosensors, Biomedical materials, Surface chemistry, Cellular biophysics