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