Unsupervised detection of high-frequency oscillations in intracranial electroencephalogram: promoting a valuable automated diagnostic tool for epilepsy
The unsupervised automated method developed in this study offers a reliable and efficient solution for detecting HFOs in iEEG signals, overcoming the limitations of manual detection processes of traditional supervised models. By providing clinicians with a clinically useful diagnostic tool, this approach holds promise for enhancing surgical resection planning in epilepsy patients and improving patient outcomes.
Authors
- Wenjing Chen (ORCID: https://orcid.org/0000-0003-3878-7681)
- Md Belal Bin Heyat (ORCID: https://orcid.org/0000-0001-5307-9582)
- Tongzhou Kang (ORCID: https://orcid.org/0000-0002-6847-3774)
- Dakun Lai (ORCID: https://orcid.org/0000-0001-9070-1721)
- Yuanning Xu
- Jamal E. Fatima
Institutions
- University of Electronic Science and Technology of China (CN)
- Sichuan University (CN)
- Westlake University (CN)
- West China Hospital of Sichuan University (CN)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2025-03-26
- DOI
- https://doi.org/10.3389/fneur.2025.1455613
- Citations
- 4
- Primary Topic
- Epilepsy research and treatment
- Type
- article
- Field-Weighted Citation Impact
- 5.88
Funders
- National Natural Science Foundation of China