Feature fusion ensemble classification approach for epileptic seizure prediction using electroencephalographic bio-signals
The proposed method not only outperforms the existing methods in terms of sensitivity and specificity but is also computationally efficient, making it suitable for real-time epileptic seizure prediction systems.
Authors
- Yazeed Alkhrijah (ORCID: https://orcid.org/0000-0001-7352-003X)
- Shehzad Khalid (ORCID: https://orcid.org/0000-0003-0899-7354)
- Muhammad Zubair (ORCID: https://orcid.org/0000-0003-2694-9364)
- Aamir Anwar (ORCID: https://orcid.org/0000-0002-2891-7844)
- Saad Arif (ORCID: https://orcid.org/0000-0002-8905-7294)
- Syed Muhammad Usman (ORCID: https://orcid.org/0000-0002-0504-3558)
- Haya Aldossary (ORCID: https://orcid.org/0009-0003-4076-1541)
- Amina Jameel (ORCID: https://orcid.org/0000-0002-1698-2227)
Institutions
- King Fahd University of Petroleum and Minerals (SA)
- Imam Mohammad ibn Saud Islamic University (SA)
- King Salman Center for Disability Research (SA)
- King Faisal University (SA)
- Bahria University (PK)
- University of Portsmouth (GB)
- Imam Abdulrahman Bin Faisal University (SA)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-08-04
- DOI
- https://doi.org/10.3389/fmed.2025.1566870
- Citations
- 2
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 2.86
Funders
- King Salman Center for Disability Research