Generative AI with WGAN-GP for boosting seizure detection accuracy
Incorporating the WGAN-GP generative AI technique for data augmentation and integrating it with bidirectional LSTM elevates seizure detection accuracy for imbalanced EEG datasets, surpassing the performance of traditional oversampling and class weight adjustment methods. This approach shows promise for improving epilepsy monitoring and management through enhanced automated detection system effectiveness.
Authors
- Neila Mezghani (ORCID: https://orcid.org/0000-0002-5935-4570)
- Imene Jemal (ORCID: https://orcid.org/0000-0002-4104-6094)
- Khadidja Henni (ORCID: https://orcid.org/0000-0002-3019-3187)
- Lina Abou-Abbas (ORCID: https://orcid.org/0000-0001-7899-5704)
Institutions
- Université TÉLUQ (CA)
- Mila - Quebec Artificial Intelligence Institute (CA)
- Lebanese American University (LB)
Publication Details
- Journal
- Frontiers in Artificial Intelligence
- Published
- 2024-10-02
- DOI
- https://doi.org/10.3389/frai.2024.1437315
- Citations
- 16
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 5.48
Funders
- Canada Research Chairs