A foundational transformer leveraging full night, multichannel sleep study data accurately classifies sleep stages
Full night, multichannel PSG representations from a foundational transformer enable accurate sleep stage classification comparable to state-of-the-art AI methods across diverse datasets.
Authors
- Patricia Kovatch (ORCID: https://orcid.org/0000-0001-8368-1742)
- Sajila Wickramaratne (ORCID: https://orcid.org/0000-0003-1045-8616)
- Girish N. Nadkarni (ORCID: https://orcid.org/0000-0001-6319-4314)
- Neomi Shah (ORCID: https://orcid.org/0009-0003-5804-0507)
- Mayte Suárez‐Fariñas (ORCID: https://orcid.org/0000-0001-8712-3553)
- Ankit Parekh (ORCID: https://orcid.org/0000-0002-5396-0553)
- Benjamin Fox (ORCID: https://orcid.org/0000-0002-5751-4884)
- Joy Jiang (ORCID: https://orcid.org/0009-0005-4414-0034)
Institutions
- Mount Sinai Health System (US)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- SLEEP
- Published
- 2025-03-06
- DOI
- https://doi.org/10.1093/sleep/zsaf061
- Citations
- 10
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 14.31
Funders
- University of Minnesota
- University of Washington
- Johns Hopkins University
- Case Western Reserve University
- York University
- National Institutes of Health
- University of California, Davis
- National Heart, Lung, and Blood Institute
- National Center for Research Resources
- National Center for Advancing Translational Sciences