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International audience; Continuous measurement of heart rate variability (HRV) in the short and ultra-short-term using wearable devices allows monitoring of physiological status and prevention of diseases. This study aims to evaluate the agreement of HRV features between a commercial device (Bora Band, Biosency) measuring photoplethysmography (PPG) and reference electrocardiography (ECG) and to assess the validity of ultra-short-term HRV as a surrogate for short-term HRV features. PPG and ECG recordings were acquired from 5 healthy subjects over 18 nights in total. HRV features include time-domain, frequency-domain, nonlinear, and visibility graph features and are extracted from 5 min 30 s and 1 min 30 s duration PPG recordings. The extracted features are compared with reference features of 5 min 30 s duration ECG recordings using repeated-measures correlation, Bland-Altman plots with 95% limits of agreements, Cliff's delta, and an equivalence test. Results showed agreement between PPG recordings and ECG reference recordings for 37 out of 48 HRV features in short-term durations. Sixteen of the forty-eight HRV features were valid and retained very strong correlations, negligible to small bias, with statistical equivalence in the ultra-short recordings (1 min 30 s). The current study concludes that the Bora Band provides valid and reliable measurement of HRV features in short and ultra-short duration recordings.

Nowadays, the elderly population represents 13% of the world's population. It is estimated that this rate will increase very rapidly leading to the phenomenon of the population ageing. This phenomenon increases the incidence of various diseases, such as cardiopulmonary pathologies. In this thesis, we are interested in the pathology known as Acute Respiratory Distress Syndrome (ARDS). ARDS is a fatal lung condition occurring in critically ill patients representing a high mortality rate. The aim of this thesis is to develop methods for the prediction of ARDS in real-time using physiological signals, such as heart rate, respiratory rate, peripheral arterial oxygen saturation and mean arterial blood pressure. During the course of this thesis, we developed three original methods for the prediction of ARDS. The first method is based on the novelty detection on each physiological signal, followed by different techniques of decision fusion. The second method consists of the extraction of parameters from the signals, selecting the most relevant ones and fusing them using the theory of belief functions. Finally, we proposed a model that combines both kernel methods and belief function theory for the prediction of ARDS. We also proposed the extraction of new parameters to complete the list used in the previous method., La population âgée représente actuellement 13% de la population mondiale. Ce taux est estimé d’augmenter très rapidement entrainant le phénomène de vieillissement de la population. Ce phénomène augmente la fréquence d’occurrence de diverses maladies, telles que les pathologies cardiopulmonaires. Dans cette thèse, nous nous intéressons à la pathologie connue sous le nom de syndrome de détresse respiratoire aigüe (SDRA). Il s’agit d’une forme très sévère de défaillance pulmonaire aiguë, consécutive à une altération de la perméabilité capillaire. Le SDRA représente un taux de mortalité élevé. Cette thèse a pour objectif de développer des méthodes de prévention du SDRA en temps réel en utilisant des signaux physiologiques, telles que le rythme cardiaque, le rythme respiratoire, le taux d'oxygène dans le sang et la moyenne de la pression artérielle. Au cours de cette thèse, nous avons développé trois méthodes originales pour la prévention du SDRA. La première méthode est basée sur la détection d’anormalité dans chaque signal physiologique, suivie d’une fusion de décisions, selon différentes approches. La deuxième méthode consiste à extraire des caractéristiques des signaux, à sélectionner les paramètres les plus pertinents et à les fusionner à l’aide de la théorie des fonctions de croyance. Finalement, nous avons proposé un modèle combinant à la fois les méthodes à noyaux et la théorie des fonctions de croyance pour la prévention du SDRA. Nous avons également proposé l’extraction de nouveaux paramètres.

International audience; Le syndrome de détresse respiratoire aigüe est une maladie qui perturbe le système respiratoire et peut entraîner la mort. Ce papier présente un modèle pour la prédiction de ce syndrome chez des patients ventilés en utilisant leurs signaux vitaux. Le modèle proposé est une approche individuelle consistant à détecter l’anormalité dans les signaux de chaque patient à comparer avec son état initial. Une décision est ensuite conçue par une fusion des décisions partielles des différents signaux analysés. Les résultats obtenus illustrent la capacité prédictive élevée de la méthode avec une sensibilité de 78% et une spécificité de 85%.

This study aims to predict Acute Respiratory Distress Syndrome (ARDS) in hospitalized patients using their physiological signals such as heart rate, breathing rate, peripheral arterial oxygen saturation and mean airway blood pressure. A data fusion approach based on hypothesis testing was developed, and applied to mechanically ventilated subjects in the MIMIC II database. By combining the information extracted from the signals using an aggregation rule, we are able to enhance the sensitivity of the ARDS prediction process. As a result, we obtained a sensitivity of up to 85% for individual signals, reaching approximately 92% using the data fusion rule.

International audience; This study aims to characterize the heart rate variability of subjects with reflex syncope during head-up tilt test and to find out non-linear parameters that can differentiate between subjects who develop syncope during the test and others who do not. For this purpose, the Empirical Mode Decomposition was applied to RR time series of subjects underwent a head-up tilt test. It decomposes the time series into waveforms called Intrinsic Mode Functions (IMF). The IMFs and their cumulative sums were used to extract several parameters as Sample Entropy, Detrended Fluctuation Analysis and indicators from Poincare plot. The results show that the proposed parameters are significantly different between the two groups in several time scales (p-value