| Titre : |
Contribution to the characterization of EEG data for epileptic seizures detection |
| Type de document : |
document électronique |
| Auteurs : |
Maria Sara Nour Sadoun, Auteur ; Taous Meriem Laleg, Directeur de thèse ; Mourad Adnane, Directeur de thèse |
| Editeur : |
[S.l.] : [s.n.] |
| Année de publication : |
2022 |
| Importance : |
1 fichier PDF (19.1 Mo) |
| Présentation : |
ill. |
| Note générale : |
Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Études : Électronique : Alger, École Nationale Polytechnique : 2022
Bibliogr. f. 102 - 108 |
| Langues : |
Anglais (eng) |
| Mots-clés : |
EEG Epileptic seizure detection Feature engineering SCSA Non linear dynamics Optimization GAN Classification |
| Index. décimale : |
PN00722 |
| Résumé : |
Epileptic seizure Detection is a challenging problem which consists in identifying a seizure among normal brain activity using electroencephalogram (EEG) signals, either by an experienced neurologist or automatically engineered frameworks. In this work, we aim to contribute to the latter to help experts in medical facilities and improve the safety and autonomy of patients. We will strive to understand the effects and contribution of each and all features. We include two types of features: SCSA and nonlinear dynamical features. We will exploit the frequency diversity of EEG and contribute to the optimization of time-embedding hyper-parameters for the dynamical features. Later on, we tackle imbalanced data by introducing 2D-Generative Adversarial Networks for Data Augmentation. Experimental results demonstrate the reliability of the workflow and performance enhancement compared to state-of-the-art accuracy, sensitivity and specificity. The three metrics approach consist scores of 0.99. This is due to two main parts: the introduction, for the first time of the SCSA to characterize epileptic seizures and the careful optimization of the time-embedding hyper-parameters for the nonlinear features. |
Contribution to the characterization of EEG data for epileptic seizures detection [document électronique] / Maria Sara Nour Sadoun, Auteur ; Taous Meriem Laleg, Directeur de thèse ; Mourad Adnane, Directeur de thèse . - [S.l.] : [s.n.], 2022 . - 1 fichier PDF (19.1 Mo) : ill. Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Études : Électronique : Alger, École Nationale Polytechnique : 2022
Bibliogr. f. 102 - 108 Langues : Anglais ( eng)
| Mots-clés : |
EEG Epileptic seizure detection Feature engineering SCSA Non linear dynamics Optimization GAN Classification |
| Index. décimale : |
PN00722 |
| Résumé : |
Epileptic seizure Detection is a challenging problem which consists in identifying a seizure among normal brain activity using electroencephalogram (EEG) signals, either by an experienced neurologist or automatically engineered frameworks. In this work, we aim to contribute to the latter to help experts in medical facilities and improve the safety and autonomy of patients. We will strive to understand the effects and contribution of each and all features. We include two types of features: SCSA and nonlinear dynamical features. We will exploit the frequency diversity of EEG and contribute to the optimization of time-embedding hyper-parameters for the dynamical features. Later on, we tackle imbalanced data by introducing 2D-Generative Adversarial Networks for Data Augmentation. Experimental results demonstrate the reliability of the workflow and performance enhancement compared to state-of-the-art accuracy, sensitivity and specificity. The three metrics approach consist scores of 0.99. This is due to two main parts: the introduction, for the first time of the SCSA to characterize epileptic seizures and the careful optimization of the time-embedding hyper-parameters for the nonlinear features. |
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