GEM-CRAP: a fusion architecture for focal seizure detection
GEM-CRAP enhances focal epilepsy detection through adaptive adjustments and attention mechanisms, achieving higher precision and robustness in complex signal environments. Beyond improving seizure interval detection, it excels in identifying and analyzing specific epileptic waveforms, such as high-frequency oscillations. This advancement may pave the way for more precise epilepsy diagnostics and provide a suitable artificial intelligence algorithm for closed-loop neurostimulation.
Authors
- Penghu Wei (ORCID: https://orcid.org/0000-0002-4529-7691)
- Lei Jin (ORCID: https://orcid.org/0000-0003-4855-2464)
- Guoguang Zhao (ORCID: https://orcid.org/0000-0002-7569-5252)
- Hang Xu (ORCID: https://orcid.org/0000-0002-2887-6587)
- Yongzhi Shan (ORCID: https://orcid.org/0000-0001-5510-1026)
- Yuanyuan Zhang (ORCID: https://orcid.org/0000-0002-9078-9062)
- Jianwei Shi
- Zhaoying Li (ORCID: https://orcid.org/0000-0003-4057-2508)
- Hengxin Dong
- Ziang Song
- Yanfeng Yang
Institutions
- Capital Medical University (CN)
Publication Details
- Journal
- Journal of Translational Medicine
- Published
- 2025-04-05
- DOI
- https://doi.org/10.1186/s12967-025-06414-5
- Citations
- 1
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 1.43
Funders
- Beijing Municipal Health Commission
- Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support
- Beijing Municipal Science and Technology Commission