Deep Clustering for Epileptic Seizure Detection
By combining deep autoencoders, SVD, and GMM, DEGM achieves superior clustering performance compared to existing methods, representing a significant advancement in biomedical research and clinical applications for epilepsy. Its robust performance on large datasets underscores its potential for improving seizure detection accuracy, ultimately contributing to better patient outcomes.
Authors
- Tala Abdallah (ORCID: https://orcid.org/0000-0003-4472-5460)
- Patrick Van Bogaert (ORCID: https://orcid.org/0000-0002-2820-0376)
- Fahed Abdallah (ORCID: https://orcid.org/0000-0002-9485-6787)
- Nisrine Jrad (ORCID: https://orcid.org/0000-0001-8177-999X)
- Anne Humeau‐Heurtier (ORCID: https://orcid.org/0000-0002-6289-0040)
- Sally El Hajjar (ORCID: https://orcid.org/0000-0001-9619-6746)
- Eliane El Howayek
Institutions
- Lebanese University (LB)
- Université catholique de l'ouest (FR)
- Centre Hospitalier Universitaire d'Angers (FR)
- Laboratoire Angevin de Recherche en Mathématiques (FR)
- Luxembourg Institute of Socio-Economic Research (LU)
- Université d'Angers (FR)
- Université de Lorraine (FR)
Publication Details
- Journal
- IEEE Transactions on Biomedical Engineering
- Published
- 2024-09-10
- DOI
- https://doi.org/10.1109/tbme.2024.3458177
- Citations
- 5
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 1.71