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| Titre: | Multi-class EEG signal classification for epileptic seizure diagnosis |
| Auteur(s): | Cherifi, Dalila
Afoun, Laid
Iloul, Zakaria
Boukerma, Billal
Adjerid, Chaouki
Boubchir, Larbi
Nait-Ali, Amine |
| Mots-clés: | EEG
Wavelet packet decomposition
Features extraction
Epilepsy diagnosis |
| Date de publication: | 2020 |
| Editeur: | Springer |
| Collection/Numéro: | International Conference in Artificial Intelligence in Renewable Energetic Systems/ (2020);pp. 635-645 |
| Résumé: | EEG signal recordings are increasingly replacing the old methods of diagnosis in medical field of many neurological disorders. Our contribution in this article is the study and development of EEG signal classification algorithms for epilepsy diagnosis using one rhythm; for classification, an optimum classifier is proposed with only when used one rhythm so that both execution time and number of features are reduced. We used wavelet packet decomposition (WPD) to extract the five rhythms of brain activity from the public Epilepsy-EEG recordings in order to represent each signal with features vector; then we applied on it the well-known classification methods. A statistical study is done to validate the different algorithms |
| URI/URL: | https://link.springer.com/chapter/10.1007/978-3-030-63846-7_60
http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/6223 |
| Collection(s) : | Communications Internationales
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