Efficient Deep Self-Supervised Learning for Epileptic Seizures Detection Using EEG Signals / Zakarya Boudraf  

Public ISBD Titre : Efficient Deep Self-Supervised Learning for Epileptic Seizures Detection Using EEG Signals Type de document : texte imprimé Auteurs : Zakarya Boudraf, Auteur ; Oussama houssem eddine Tamourt, Auteur ; Abdelouaheb Moussaoui, Directeur de thèse Année de publication : 2023 Importance : 1 vol (48 f .) Format : 29cm Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : Self-supervised epilepsy Index. décimale : 004 Informatique Résumé : When dealing with epileptic seizures electroencephalogram’s (EEG) can provide essential information about electrical brain activity. Patients with epilepsy produce an excess of electrical discharges, causing involuntary body movements along with other unpleasant symptoms. That information allows us to detect these seizures and help minimize the damage done. However, an EEG requires extensive knowledge of signal processing in order for medical practitioners to correctly diagnose a patient. That’s why in recent years machine learning algorithms have been employed to automatically detect these seizures. But, in order to train these models, a large dataset of labeled data is needed which is both expensive and time consuming. In this paper, we propose three self-supervised learning models that make use of unlabeled data, while still providing accurate and efficient predictions. Our model performs competitively with previously published works by achieving an accuracy of 99.08%, sensitivity of 83.33%, and a FPR of 0.06. Côte titre : MAI/0700 En ligne : https://drive.google.com/file/d/1FUXzTw8ZQSN8HsU2J9S4jjfMtCBnM1b2/view?usp=drive [...] Format de la ressource électronique : pdf Efficient Deep Self-Supervised Learning for Epileptic Seizures Detection Using EEG Signals [texte imprimé] / Zakarya Boudraf, Auteur ; Oussama houssem eddine Tamourt, Auteur ; Abdelouaheb Moussaoui, Directeur de thèse . - 2023 . - 1 vol (48 f .) ; 29cm. Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : Self-supervised epilepsy Index. décimale : 004 Informatique Résumé : When dealing with epileptic seizures electroencephalogram’s (EEG) can provide essential information about electrical brain activity. Patients with epilepsy produce an excess of electrical discharges, causing involuntary body movements along with other unpleasant symptoms. That information allows us to detect these seizures and help minimize the damage done. However, an EEG requires extensive knowledge of signal processing in order for medical practitioners to correctly diagnose a patient. That’s why in recent years machine learning algorithms have been employed to automatically detect these seizures. But, in order to train these models, a large dataset of labeled data is needed which is both expensive and time consuming. In this paper, we propose three self-supervised learning models that make use of unlabeled data, while still providing accurate and efficient predictions. Our model performs competitively with previously published works by achieving an accuracy of 99.08%, sensitivity of 83.33%, and a FPR of 0.06. Côte titre : MAI/0700 En ligne : https://drive.google.com/file/d/1FUXzTw8ZQSN8HsU2J9S4jjfMtCBnM1b2/view?usp=drive [...] Format de la ressource électronique : pdf

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
MAI/0700	MAI/0700	Mémoire	Bibliothéque des sciences	Anglais	Disponible Disponible

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