Deep Learning for Sleep Apnea Detection and Prediction

Sleep apnea is sleep-related breathing disorder affecting millions of people around the world - 2-4 % of the adult population. It is characterized with abnormal breathing events during sleep, which can be divided into two main types: obstructive apnea (complete or almost complete cessation in breathing) and hypopnea events (partial reduction in breathing). People with sleep apnea suffer from loud snoring, excessive daytime sleepiness, fatigue, irritability, morning headache, memory loss, chocking and gasping for breath during sleep. If left untreated, sleep apnea increases the risk of heart attack, diabetes, depression, and even early death. Detecting obstructive apnea and hypopnea events through manual inspection of Polysomnography (PSG) recordings is very labor intensive and time consuming as the recordings are long, complex, and multi-channel. It is also expensive as it requires the expertise of highly trained physicians and sleep experts. Moreover, detecting these events using the traditional machine learning approaches requires extraction and selection of suitable features from several respiratory PSG channels, that are then used as inputs to a classification model. In this thesis, we propose new approaches for automatic detection and prediction of sleep apnea events, based on deep learning, that require minimal human intervention. We firstly propose new deep learning approaches based on Convolutional Neural Networks (CNNs) to automatically detect obstructive apnea and hypopnea events from a single PSG respiratory channel (nasal airflow), and then from multiple PSG respiratory channels (nasal airflow, thoracic, abdominal). The proposed approaches are directly applied to the raw data of PSG signals, without feature engineering, and can be used to aid physicians and sleep experts in sleep apnea diagnosis. Next, we propose the use of Convolutional Autoencoders (CAEs), a deep learning unsupervised technique, for feature learning and data compression of the respiratory biosignals, used in conjunction with CNN for sleep apnea detection. The proposed CAE methods are compared with other unsupervised feature reduction techniques. We show that the reduced features from CAE can give high performance and are hence useful for the design of low-cost and portable devices for sleep monitoring. Finally, we propose novel methods for predicting sleep apnea events in advance based on CNNs and Markov chains, using data from the three respiratory channels (nasal airflow, abdominal, thoracic). The proposed methods could be used in practical applications for patient monitoring and reducing the number of apnea events during sleep. Our proposed approaches were evaluated using a large dataset from 1,507 subjects and the results show their effectiveness

Évaluation d'impact de l'explosion du port de Beyrouth: Étude multidimensionnelle des incidences socio-économiques des explosions du 4 août 2020 de Beyrouth

Ce rapport a été financé par l'Institut français du Proche-Orient. Les analyses et conclusions de ce document sont formulées sous la responsabilité de leurs auteurs. Elles ne reflètent pas nécessairement le point de vue de l'Ifpo ni de leurs institutions partenaires.Les explosions du 4 août 2020 au port de Beyrouth ont plongé le Liban dans une détresse insoutenable. Cette étude a vocation à mesurer l’impact de l’explosion en considérant le point de vue des victimes des événements du 4 août 2020. Ce travail d’évaluation vient compléter les travaux et les rapports internationaux sur la quantification des dommages et des pertes causées, en mesurant les incidences de l’explosion à l’échelle sectorielle et dans le micro-social. Ce travail se concentre donc sur les préjudices causés aux travailleurs et aux habitants, plutôt que de se limiter aux effets sur les entreprises et l’habitat. Cette étude regroupe ainsi un ensemble de recherches sur les quartiers endommagés par l’explosion, afin de mettre en commun des analyses multidimensionnelles de son impact et de réfléchir aux pistes d’action prioritaires. Les conclusions de ce rapport ont pour but d’identifier les besoins des habitants et des travailleurs dans la zone de l’explosion, dans l’optique d’établir un ordre de priorité et d’orienter les futures recherches