Mamba-based deep learning approach for sleep staging on a wireless multimodal wearable system without electroencephalography
Our Mamba-based deep learning model can successfully infer major sleep stages from the ANNE One, a wearable system without electroencephalography (EEG), and can be applied to data from adults attending a tertiary care sleep clinic.
Authors
- Chang Li (ORCID: https://orcid.org/0000-0002-3267-1859)
- R. Yin
- Dharmendra Gurve (ORCID: https://orcid.org/0000-0002-7025-9451)
- Alex He-Mo
- J. Andrew Zhang (ORCID: https://orcid.org/0000-0002-6102-3762)
- Yuzhi Tang
- Maged Goubran
- Andrew S P Lim
- Aron S Buchman
- Veronique van der Horst
- Bo Wang
Institutions
- Rush University Medical Center (US)
- Boston Medical Center (US)
- Beth Israel Deaconess Medical Center (US)
- Sunnybrook Health Science Centre (CA)
- University Health Network (CA)
- University of Toronto (CA)
- Queen's University (CA)
- Vector Institute (CA)
- Sunnybrook Hospital (CA)
Publication Details
- Journal
- SLEEP
- Published
- 2026-02-05
- DOI
- https://doi.org/10.1093/sleep/zsag022
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 0.00