Letteratura scientifica selezionata sul tema "Classification d'état du sommeil"
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Articoli di riviste sul tema "Classification d'état du sommeil"
El-Sayed, Atef. "LE BUDGET D'ÉTAT DE LA R.A.U.: DÉVELOPPEMENT ET CLASSIFICATION". Review of Income and Wealth 1963, n. 1 (5 aprile 2006): 177–234. http://dx.doi.org/10.1111/j.1475-4991.1965.tb01027.x.
Testo completoMontplaisir, J., O. Lapierre e G. Lavigne. "Les troubles moteurs au cours du sommeil: essai de classification". Neurophysiologie Clinique/Clinical Neurophysiology 24, n. 2 (aprile 1994): 155–59. http://dx.doi.org/10.1016/s0987-7053(94)80005-7.
Testo completoZandy, Moe, Vicky Chang, Deepa P. Rao e Minh T. Do. "Exposition à la fumée du tabac et sommeil : estimation de l’association entre concentration de cotinine urinaire et qualité du sommeil". Promotion de la santé et prévention des maladies chroniques au Canada 40, n. 3 (marzo 2020): 77–89. http://dx.doi.org/10.24095/hpcdp.40.3.02f.
Testo completoKerkeni, N., R. Ben Cheikh, M. H. Bedoui, F. Alexandre e M. Dogui. "Classification des stades de sommeil par des réseaux de neurones artificiels hiérarchiques". IRBM 33, n. 1 (febbraio 2012): 35–40. http://dx.doi.org/10.1016/j.irbm.2011.12.006.
Testo completoDecat, Nicolas, Jasmine Walter, Zhao Koh, Piengkwan Sribanditmongkol, Ben Fulcher, Jennifer Windt, Thomas Andrillon e Naotsugu Tsuchiya. "Au-delà de la classification traditionnelle du sommeil : extraction massive et classification non supervisée des données polysomnographiques". Médecine du Sommeil 19, n. 1 (marzo 2022): 51. http://dx.doi.org/10.1016/j.msom.2022.01.185.
Testo completoShults, E. E. "On the classification of revolutions". RUDN Journal of Sociology 19, n. 3 (15 dicembre 2019): 406–18. http://dx.doi.org/10.22363/2313-2272-2019-19-3-406-418.
Testo completoMartin, Claude. "La comparaison des systèmes de protection sociale en Europe. De la classification à l’analyse des trajectoires d’État providence". Lien social et Politiques, n. 37 (2 ottobre 2002): 145–55. http://dx.doi.org/10.7202/005185ar.
Testo completoRey, M., e S. Royant-Parola. "Les insomnies à la lumière de la nouvelle classification internationale des troubles du sommeil". Médecine du Sommeil 4, n. 11 (marzo 2007): 41–43. http://dx.doi.org/10.1016/s1769-4493(07)70057-2.
Testo completoMuzet, A., S. Werner, T. Roth, J. Y. Schaffhauser, R. Fleck e R. Luthringer. "Classification du sommeil à l’aide de la fréquence cardiaque et des mouvements du poignet". Médecine du Sommeil 11, n. 1 (gennaio 2014): 29–30. http://dx.doi.org/10.1016/j.msom.2014.01.069.
Testo completoGhiglione, Rodolphe, M. Bromberg, Edouard Friemel, Christiane Kekenbosch e Jean-Claude Verstiggel. "Prédications d'état, de déclaration et d'action : essai de classification en vue d'une application en analyse de contenu". Langages 25, n. 100 (1990): 81–100. http://dx.doi.org/10.3406/lgge.1990.1568.
Testo completoTesi sul tema "Classification d'état du sommeil"
Lerogeron, Hugo. "Approximation de Dynamic Time Warping par réseaux de neurones pour la compression de signaux EEG et l'analyse de l'insomnie induite par le COVID long". Electronic Thesis or Diss., Normandie, 2023. http://www.theses.fr/2023NORMR098.
Testo completoThis manuscript presents the work carried out within the framework of the CIFRE thesis conducted in partnership between LITIS and Saagie and which is part of the PANDORE-IA project in association with the VIFASOM sleep center.Electroencephalographic (EEG) signals are very useful in helping experts identify various abnormalities like sleep disorders. Recently, the community has shown great interest in long COVID and its various impacts on sleep. However, these signals are voluminous: compression allows reducing storage and transfer costs. Recent compression approaches are based on autoencoders that use a cost function to learn. It is usually the Mean Squared Error (MSE), but there are metrics more suited to time series, particularly Dynamic Time Warping (DTW). However, DTW is not differentiable and thus can not be used as a loss for end-to-end learning.To solve this problem, we propose in this thesis two approaches to approximate DTW based on neural networks. The first approach uses a Siamese network to project the signals so that the Euclidean distance of the projected signals is as close as possible to the DTW of the original signals. The second approach attempts to predict directly the DTW value. We show that these approaches are faster than other differentiable approximations of DTW while obtaining results similar to DTW in query or classification on sleep data.We then demonstrate that the Siamese approximation can be used as a cost function for learning a sleep signal compression system based on an autoencoder. We justify the choice of the network architecture by the fact that it allows us to vary the compression rate. We evaluate this compression system by classification on the compressed and then reconstructed signals, and show that the usual measures of compression quality do not allow for a proper assessment of a compression system's ability to retain discriminative information. We show that our DTW approximations yield better performance on the reconstructed data than conventional compression algorithms and other reconstruction losses.Finally, to study the impact of long COVID on insomnia, we collect and provide the community with a dataset named COVISLEEP, containing polysomnographies of individuals who developed chronic insomnia after COVID infection, and of those suffering from chronic insomnia but who have not been infected by the virus. We compare various state-of-the-art approaches for sleep staging, and use the best one for learning the detection of long COVID. We highlight the difficulty of the task, especially due to the high variability among patients. This offers a complex dataset to the community that allows for the development of more effective methods
Bruno, Stéphane. "Modélisation de signaux physiologiques en vue d'une classification automatique du sommeil". Rennes 1, 2006. http://www.theses.fr/2006REN1S002.
Testo completoBruno, Stéphane Scalart Pascal. "Modélisation de signaux physiologiques en vue d'une classification automatique du sommeil". [S.l.] : [s.n.], 2006. ftp://ftp.irisa.fr/techreports/theses/2006/bruno.pdf.
Testo completoZoubek, Lukas. "Classification automatique d'enregistrements de sommeil humain combiant l'identification d'artefacts et la sélection de caractéristiques pertinentes". Phd thesis, Grenoble 1, 2008. http://www.theses.fr/2008GRE10063.
Testo completoThis thesis engages in automatic analysis of human sleep. It mainly focuses on the development of an automatic system for classification of polysomnographic recordings, composed of three signals: EEG, EOG and EMG. This thesis proposes a complex classification system, which is capable to deal with various artifacts possibly present in the physiological signals and which uses the most relevant parameters computed from the analyzed signals. The first part of this thesis presents a procedure to automatically identify eight common artifacts in 2-sec segments of the analyzed signals. Then, a strategy is applied in order to evaluate quality of the signals characterizing each 20-sec epoch of the recording. In the second part of this thesis, an iterative feature selection method is proposed and applied on a large database of polysomnographic recordings, so as to select the most relevant parameters that will serve as inputs for the automatic classifier. Then, as a result of the two first parts, a complex two-step sleep/wake stages automatic classification system is proposed. In a first step, an artifact detection system selects the artifact-free polysomnographic signals in the epoch to be scored. In the second step, the features selected as the most relevant are extracted from the artifact-free signals and classified using a neural network classifier chosen among a bank of four classifiers, which differs one from the others by the signals used. Thus, the final classification system allows classification using relevant features computed from artifact-free signals, without loosing many
Zoubek, Lukas. "Classification automatique d'enregistrements de sommeil humain combiant l'identification d'artefacts et la sélection de caractéristiques pertinentes". Phd thesis, Université Joseph Fourier (Grenoble), 2008. http://tel.archives-ouvertes.fr/tel-00283929.
Testo completoLa première partie de la thèse présente une procédure permettant l'identification automatique, sur des plages de signaux de 2 secondes, de 8 types d'artéfacts parmi les plus courants ainsi qu'une stratégie permettant d'évaluer la qualité globale d'un signal sur une période de 20 secondes.
Dans une deuxième partie, une méthode de sélection de caractéristiques est proposée puis appliquée sur une base de signaux, afin de sélectionner les caractéristiques qui serviront d'entrées au classifieur.
Enfin, en conséquence des deux premières parties, un système de classification automatique à deux étapes est proposé. Dans une première étape, un système de détection d'artéfacts permet de sélectionner les signaux ne présentant pas d'artéfacts au cours de l'epoch à classer. Dans la deuxième étape, les caractéristiques les plus discriminantes sont extraites et classées à l'aide d'un réseau de neurones sélectionné parmi un ensemble de quatre classifieurs, chaque classifieur utilisant des caractéristiques d'entrées extraites de combinaisons de signaux différentes. Le système proposé permet la classification des enregistrements de nuits de sommeil à partir de caractéristiques extraites de signaux non pollués par des artefacts, sans perdre un trop grand nombre d'epochs.
Muller, Bruno. "Transfer Learning through Kernel Alignment : Application to Adversary Data Shifts in Automatic Sleep Staging". Thesis, Troyes, 2021. http://www.theses.fr/2021TROY0037.
Testo completoThis doctoral project aims at improving an automatic sleep staging system by taking into account inter-and-intra-individual variabilities, the latter having adversary effects on the classification. We focus on the detection of Rapid-Eye Movement periods during sleep. The core of our research is transfer learning and the selection of suitable detector(s) among a set, allowing the individualisation of the analysis by the exploitation of the observed data properties. We focus on the application of kernel alignment methods, firstly through the use of kernel-target alignment, studied here in a dual way, i.e. the kernel is fixed and the criterion is optimised with respect to the sought target labels. In a second step, we introduced kernel-cross alignment, allowing to take more efficiently advantage of the information contained in the training data. The ideas developed in the framework of this work have been extended to automatically selecting one or more efficient training sets for a given test set. The contributions of this work are both methodological and algorithmic, general in scope, but also focused on the application
Sorosac, Nicole. "Etude d'un système d'inspection optique d'état de surface de bobines d'acier inoxydable laminées à froid". Grenoble 1, 1988. http://www.theses.fr/1988GRE10164.
Testo completoSchaltenbrand, Nicolas. "Élaboration d'un système d'analyse automatique du sommeil par des méthodes de reconnaissance de formes". Compiègne, 1990. http://www.theses.fr/1990COMPD313.
Testo completoVanbuis, Jade. "Analyse automatique des stades du sommeil à partir des voies électrophysiologiques et cardiorespiratoires". Thesis, Le Mans, 2021. http://cyberdoc-int.univ-lemans.fr/Theses/2021/2021LEMA1004.pdf.
Testo completoThe diagnostic of sleep-disordered breathing requires the analysis of various signals obtained while recording sleep. The analysis is carried by a sleep specialist, which studies the patient's ventilation and, depending on the diagnostic tool used for the record, sleep stages. Sleep stage scoring is a complex and time-consuming task. Three diagnosis support algorithms dedicated to this task are presented in this thesis.The first one provides a wakefulness versus sleep classification, designed for a new diagnostic tool. It results in the ability to make a precise diagnosis of sleep apnea syndrome, at low cost.The second algorithm, based on electrophysiological channels, provides a full sleep stage classification while using the most complete diagnosis tool. It was implemented considering the known limitations for the use of algorithms in clinical practice. Its architecture thus reproduces the manual scoring process. A self-adaptative thresholding function was also implemented to provide a patient-dependent classification. The obtained results are comparable with the ones from sleep experts.The third algorithm, based on cardio-respiratory channels, provides a sleep stage classification while using a diagnostic tool that is insufficient for a manual sleep scoring, yet still highly used. The task is challenging but the obtained results are satisfying compared to literature.All three algorithms, which were designed for various diagnostic tools, will help sleep experts analyzing sleep
Rahmani, Naïm Mohamed. "Instrumentation pour le traitement numérique du signal électroencéphalographique : Application à la reconnaissance automatique, temps réel, des différents stades de sommeil et de veille chez le rat". Nancy 1, 1990. http://docnum.univ-lorraine.fr/public/SCD_T_1990_0336_RAHMANI.pdf.
Testo completoLibri sul tema "Classification d'état du sommeil"
Roberge, Michel. La gestion de l'information administrative: Application globale, systémique et systématique. Québec: Documentor, 1992.
Cerca il testo completoCassiodorus, Senator, ca. 487-ca. 580., Halporn James W e Vessey Mark, a cura di. Institutions of divine and secular learning: And, On the soul. Liverpool: Liverpool University Press, 2004.
Cerca il testo completoSecrecy: The American experience. New Haven: Yale University Press, 1998.
Cerca il testo completoMoynihan, Daniel P. Secrecy: The American experience. New Haven, Conn: Yale University Press, 1998.
Cerca il testo completoProduction of Secrecy: States of Opacity. Taylor & Francis Group, 2021.
Cerca il testo completoCapitoli di libri sul tema "Classification d'état du sommeil"
Staner, Luc. "Chapitre 1. Classification des troubles du sommeil". In Sommeil et psychiatrie, 1–15. Dunod, 2016. http://dx.doi.org/10.3917/dunod.schro.2016.01.0004.
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