Deep learning‐based classification and segmentation of interictal epileptiform discharges using multichannel <scp>electroencephalography</scp>
In this paper, we aim to present an artificial intelligence-based toolbox for IED detection, which may facilitate epilepsy diagnosis at the bedside in the future. U-IEDNet demonstrates great potential to improve the accuracy and efficiency of IED detection in multichannel EEG recordings.
Authors
- Yulin Sun (ORCID: https://orcid.org/0000-0003-3097-7755)
- Xiuyun Liu (ORCID: https://orcid.org/0000-0001-9540-4865)
- Fengling Feng (ORCID: https://orcid.org/0009-0002-4326-413X)
- Huan Zhou (ORCID: https://orcid.org/0000-0002-9396-8787)
- Lian Huang (ORCID: https://orcid.org/0000-0002-0416-7532)
- Ming Dong (ORCID: https://orcid.org/0000-0001-6715-3405)
- Runnan He (ORCID: https://orcid.org/0000-0003-2137-8785)
- Xiaoguang Tong (ORCID: https://orcid.org/0000-0003-4774-8625)
- Min Guan
- Xun Chen (ORCID: https://orcid.org/0000-0002-6563-3795)
Institutions
- University of Science and Technology of China (CN)
- Tianjin University (CN)
- Tianjin Huanhu Hospital (CN)
- Human Computer Interaction (Switzerland) (CH)
- First Affiliated Hospital of Jinan University (CN)
- Tianjin Medical University (CN)
Publication Details
- Journal
- Epilepsia
- Published
- 2025-05-24
- DOI
- https://doi.org/10.1111/epi.18463
- Citations
- 4
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 5.72
Funders
- Chinese Academy of Sciences
- Tianjin University
- Austrian Institute of Technology
- Key Technologies Research and Development Program
- National Defense Science and Technology Innovation Fund of the Chinese Academy of Sciences