KAN-SleepNet: A deep learning model combining Kolmogorov–Arnold Networks and bidirectional LSTM for automated sleep staging using EEG signals
The KAN-SleepNet demonstrates superior performance in automated sleep staging, highlighting its potential as an efficient and supportive tool for clinical sleep analysis.
Authors
- Yu‐Wan Yang (ORCID: https://orcid.org/0009-0005-5612-4736)
- Yunsong Peng (ORCID: https://orcid.org/0000-0002-6328-3189)
- Ying‐Lin Zhou (ORCID: https://orcid.org/0000-0002-7785-3250)
- Zhenliang Xiong (ORCID: https://orcid.org/0000-0003-1696-4499)
- Yaqing Gou (ORCID: https://orcid.org/0000-0001-5766-0956)
- Yan Gong
- Rongpin Wang (ORCID: https://orcid.org/0000-0001-7587-4181)
- Xianchun Zeng (ORCID: https://orcid.org/0000-0003-0857-3834)
- Rui Xu (ORCID: https://orcid.org/0000-0003-1377-9298)
Institutions
- Guiyang Medical University (CN)
- Guizhou University (CN)
- Guizhou Provincial People's Hospital (CN)
Publication Details
- Journal
- Digital Health
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1177/20552076251398440
- Citations
- 1
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 1.43