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Written and edited by leading clinicians and researchers in sleep medicine, this is the first book to focus on the causes, consequences and treatment of disorders of excessive sleepiness. Extensive coverage is provided for all known causes of sleepiness, including sleep deprivation, obstructive sleep apnea syndrome, narcolepsy and other hypersomnias of central origin, shift work, and medical and psychiatric disorders. Since many causes of sleepiness are difficult to differentiate from each other, and treatment modalities can vary greatly from one disorder to another, this book helps the clinician to formulate a differential diagnosis that will ultimately lead to the correct diagnosis. Epidemiology, evaluation of the sleepy patient, diagnostic investigations including neuroimaging, subjective and objective testing, cognitive effects of sleepiness, motor vehicle driving issues, medico-legal aspects of sleepiness, and therapy are also discussed in detail. This is an essential resource for neurologists, psychiatrists and sleep specialists.

Drowsiness.

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The aim of this thesis consists of the development of an automatic drowsiness monitoring system based on the electrocardiogram (ECG). Moreover, as the feasibility of this physiological signal to detect drowsiness is still not proved, this thesis also investigates its feasibility.

This thesis is based on an experiment were subjects were sleep deprived during 28 hours. At 3 specific moments of sleep deprivation, subjects performed psychomotor vigilance task (PVT). During these tasks, different physiological signals whose electroencephalogram (EEG), electrooculogram (EOG), and electrocardiogram (ECG) were recorded. Based on the EEG and EOG signals, which are the references to assess drowsiness, the true state of each subject is known on the Karolinska Drowsiness Scale and can be defined as awake or drowsy given a defined threshold. 

First, this thesis performs a review of the literature to find the possible parameters indicative of drowsiness computed from the ECG. Then, a complete processing chain of the ECG signal is implemented to be able to compute these parameters in the time and statistical domains, the non-linear domain, and finally in the frequency domain from the raw ECG of the subjects. As the respiratory signal can be derived from the ECG (ECG-Derived Respiration signal), this thesis also incorporates parameters from the respiratory domain in order to see if this domain can be use to detect drowsiness.

Once these parameters are computed, a machine learning phase is developed. During this phase, the issue of the variability of the features between the subjects was highlighted. Several techniques to compensate this variability have been tested but none improved the results obtained. This variability makes the system developed to be not reliable enough on all the subjects of the experiment to only use the ECG to predict drowsiness.

Drowsiness, ECG --- Ingénierie, informatique & technologie > Ingénierie électrique & électronique

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Trucking --- Wakefulness --- Drowsiness --- Fatigue --- Truck drivers --- Safety measures --- Physiological aspects. --- Measurement. --- Physiology.

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This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others.

Information technology industries --- sleep stage scoring --- neural network-based refinement --- residual attention --- T-end annotation --- signal quality index --- tSQI --- optimal shrinkage --- emotion --- EEG --- DEAP --- CNN --- surgery image --- disgust --- autonomic nervous system --- electrocardiogram --- galvanic skin response --- olfactory training --- psychophysics --- smell --- wearable sensors --- wine sensory analysis --- accuracy --- convolution neural network (CNN) --- classifiers --- electrocardiography --- k-fold validation --- myocardial infarction --- sensitivity --- sleep staging --- electroencephalography (EEG) --- brain functional connectivity --- frequency band fusion --- phase-locked value (PLV) --- wearable device --- emotional state --- mental workload --- stress --- heart rate --- eye blinks rate --- skin conductance level --- emotion recognition --- electroencephalogram (EEG) --- photoplethysmography (PPG) --- machine learning --- feature extraction --- feature selection --- deep learning --- non-stationarity --- individual differences --- inter-subject variability --- covariate shift --- cross-participant --- inter-participant --- drowsiness detection --- EEG features --- drowsiness classification --- fatigue detection --- residual network --- Mish --- spatial transformer networks --- non-local attention mechanism --- Alzheimer’s disease --- fall detection --- event-centered data segmentation --- accelerometer --- window duration --- n/a --- Alzheimer's disease

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This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others.

sleep stage scoring --- neural network-based refinement --- residual attention --- T-end annotation --- signal quality index --- tSQI --- optimal shrinkage --- emotion --- EEG --- DEAP --- CNN --- surgery image --- disgust --- autonomic nervous system --- electrocardiogram --- galvanic skin response --- olfactory training --- psychophysics --- smell --- wearable sensors --- wine sensory analysis --- accuracy --- convolution neural network (CNN) --- classifiers --- electrocardiography --- k-fold validation --- myocardial infarction --- sensitivity --- sleep staging --- electroencephalography (EEG) --- brain functional connectivity --- frequency band fusion --- phase-locked value (PLV) --- wearable device --- emotional state --- mental workload --- stress --- heart rate --- eye blinks rate --- skin conductance level --- emotion recognition --- electroencephalogram (EEG) --- photoplethysmography (PPG) --- machine learning --- feature extraction --- feature selection --- deep learning --- non-stationarity --- individual differences --- inter-subject variability --- covariate shift --- cross-participant --- inter-participant --- drowsiness detection --- EEG features --- drowsiness classification --- fatigue detection --- residual network --- Mish --- spatial transformer networks --- non-local attention mechanism --- Alzheimer’s disease --- fall detection --- event-centered data segmentation --- accelerometer --- window duration --- n/a --- Alzheimer's disease