A study of EEG feature complexity in epileptic seizure prediction [r-libre/2844]

Jemal, Imene; Mitiche, Amar et Mezghani, Neila (2021). A study of EEG feature complexity in epileptic seizure prediction. Applied Sciences, 11 (4), 1579. https://doi.org/10.3390/app11041579

Fichier(s) associé(s) à ce document :

PDF - Jemal2022_3.pdf
Contenu du fichier : Version de l'éditeur
Licence : Creative Commons CC BY.

Télécharger

Catégorie de document :	Articles de revues
Évaluation par un comité de lecture :	Oui
Étape de publication :	Publié
Résumé :	The purpose of this study is (1) to provide EEG feature complexity analysis in seizure prediction by inter-ictal and pre-ital data classification and, (2) to assess the between-subject variability of the considered features. In the past several decades, there has been a sustained interest in predicting epilepsy seizure using EEG data. Most methods classify features extracted from EEG, which they assume are characteristic of the presence of an epilepsy episode, for instance, by distinguishing a pre-ictal interval of data (which is in a given window just before the onset of a seizure) from inter-ictal (which is in preceding windows following the seizure). To evaluate the difficulty of this classification problem independently of the classification model, we investigate the complexity of an exhaustive list of 88 features using various complexity metrics, i.e., the Fisher discriminant ratio, the volume of overlap, and the individual feature efficiency. Complexity measurements on real and synthetic data testbeds reveal that that seizure prediction by pre-ictal/inter-ictal feature distinction is a problem of significant complexity. It shows that several features are clearly useful, without decidedly identifying an optimal set.
Adresse de la version officielle :	https://www.mdpi.com/2076-3417/11/4/1579
Déposant:	Ayena, Johannes
Responsable :	Johannes Ayena
Dépôt :	16 janv. 2023 19:35
Dernière modification :	16 janv. 2023 19:35

Actions (connexion requise)

RÉVISER