A channel-wise attention-based representation learning method for epileptic seizure detection and type classification - Université Paris-Est-Créteil-Val-de-Marne Accéder directement au contenu
Article Dans Une Revue Journal of Ambient Intelligence and Humanized Computing Année : 2023

A channel-wise attention-based representation learning method for epileptic seizure detection and type classification

Asma Baghdadi
Rahma Fourati
  • Fonction : Auteur
Yassine Aribi
  • Fonction : Auteur
Sawsen Daoud
  • Fonction : Auteur
Mariem Dammak
  • Fonction : Auteur
Chokri Mhiri
  • Fonction : Auteur
Habib Chabchoub
  • Fonction : Auteur
Adel Alimi
  • Fonction : Auteur

Résumé

Epilepsy affect almost 1% of the worldwide population. An early diagnosis of seizure types is a patient-dependent process which is crucial for the treatment selection process. The selection of the proper treatment relies on the correct identification of seizures type. As such, identifying the seizure type has the biggest immediate influence on therapy than the seizure detection, reducing the neurologist’s efforts when reading and detecting seizures in EEG recordings. Most of the existing seizure detection and classification methods are conceptualized following the patient-dependent schema thus fail to perform well with unknown cases. Our work focuses on patient-independent schema for seizure type classification and pays more attention to the explainability of the underlying attention mechanism of our method. Using a channel-wise attention mechanism, a quantification of the EEG channels contribution is enabled. Therefore, results become more interpretable and a visualization of brain lobes contribution by seizure types is allowed. We evaluate our model for seizure detection and type classification on CHB-MIT and the recently released TUH EEG Seizure, respectively. Our model is able to classify 8 seizure types with an accuracy of 98.41%, directly from raw EEG data without any preprocessing. A case study showed a high correlation between the neurological baselines and the interpretable results of our model.

Dates et versions

hal-04509171 , version 1 (18-03-2024)

Identifiants

Citer

Asma Baghdadi, Rahma Fourati, Yassine Aribi, Sawsen Daoud, Mariem Dammak, et al.. A channel-wise attention-based representation learning method for epileptic seizure detection and type classification. Journal of Ambient Intelligence and Humanized Computing, 2023, 14 (7), pp.9403-9418. ⟨10.1007/s12652-023-04609-6⟩. ⟨hal-04509171⟩

Collections

LISSI UPEC
5 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More