Deep Feature Learning (Extraction and Generation) Using a Bidirectional LSTM-CNN and Deep Generative Models Applied to Physiological Signals (EEG/ECG) Classification / Hichem Betiche  

Public ISBD Titre : Deep Feature Learning (Extraction and Generation) Using a Bidirectional LSTM-CNN and Deep Generative Models Applied to Physiological Signals (EEG/ECG) Classification Type de document : texte imprimé Auteurs : Hichem Betiche, Auteur ; Youcef Oualid Zemame, Auteur ; Abdelouahab Moussaoui, Directeur de thèse Année de publication : 2022 Importance : 1 vol (60 f .) Format : 29cm Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : Informatique Index. décimale : 004 Informatique Résumé : In this thesis, we want to propose some classification models based on deep learning that will be able to classify and extract features from EEG/ECG signals with high accuracy . Electroencephalogram or EEG is related to the brain and electrocardiogram or ECG is related to the heart. EEG is the equipment used for measuring electrical activities of the brain. On the other hand, ECG is used for measuring activities of heart. We have tried many models which are tested and verified using the public datasets : mitbih arrhythmia and EEG in schizophrenia ,We have used Variational autoencoders to solve the unbalance of MIT dataset then a comparative study was made between different deep learning models and the result showed that the deep approach with Bi-lstm cnn gave the best test accuracy where we obtained an accuracy up to 98% for ECG dataset and 84% for EEG dataset Côte titre : MAI/0593 En ligne : https://drive.google.com/file/d/1CWN7ejvxw2JSsLBrnr-hBmkqu7OE2-Jj/view?usp=share [...] Format de la ressource électronique : pdf Deep Feature Learning (Extraction and Generation) Using a Bidirectional LSTM-CNN and Deep Generative Models Applied to Physiological Signals (EEG/ECG) Classification [texte imprimé] / Hichem Betiche, Auteur ; Youcef Oualid Zemame, Auteur ; Abdelouahab Moussaoui, Directeur de thèse . - 2022 . - 1 vol (60 f .) ; 29cm. Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : Informatique Index. décimale : 004 Informatique Résumé : In this thesis, we want to propose some classification models based on deep learning that will be able to classify and extract features from EEG/ECG signals with high accuracy . Electroencephalogram or EEG is related to the brain and electrocardiogram or ECG is related to the heart. EEG is the equipment used for measuring electrical activities of the brain. On the other hand, ECG is used for measuring activities of heart. We have tried many models which are tested and verified using the public datasets : mitbih arrhythmia and EEG in schizophrenia ,We have used Variational autoencoders to solve the unbalance of MIT dataset then a comparative study was made between different deep learning models and the result showed that the deep approach with Bi-lstm cnn gave the best test accuracy where we obtained an accuracy up to 98% for ECG dataset and 84% for EEG dataset Côte titre : MAI/0593 En ligne : https://drive.google.com/file/d/1CWN7ejvxw2JSsLBrnr-hBmkqu7OE2-Jj/view?usp=share [...] Format de la ressource électronique : pdf

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
MAI/0593	MAI/0593	Mémoire	Bibliothéque des sciences	Anglais	Disponible Disponible

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