A deep hybrid CSAE-GRU framework with two-stage balancing for automatic epileptic seizure detection using EEG-derived features
This study introduces a novel framework combining cluster centroid-based under-sampling, BLSMOTE oversampling, and transfer learning via CSAE-GRU integration. The method offers a promising direction for reliable and clinically applicable automated epilepsy diagnosis.
Authors
- Wenna Chen (ORCID: https://orcid.org/0000-0003-2543-8271)
- Xiang Fei (ORCID: https://orcid.org/0000-0002-0877-7047)
- Mingyue Liu (ORCID: https://orcid.org/0009-0007-6751-1808)
- Ganqin Du (ORCID: https://orcid.org/0000-0001-9097-2505)
- Jincan Zhang (ORCID: https://orcid.org/0000-0002-8317-764X)
- Shaojie Zheng (ORCID: https://orcid.org/0000-0002-7833-1497)
Institutions
- Henan University of Science and Technology (CN)
- First Affiliated Hospital of Henan University of Science and Technology (CN)
Publication Details
- Journal
- Frontiers in Neuroscience
- Published
- 2025-11-11
- DOI
- https://doi.org/10.3389/fnins.2025.1698960
- Citations
- 1
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 1.43