Mortality risk assessment using deep learning-based frequency analysis of electroencephalography and electrooculography in sleep
Spectral power features, possibly reflecting abnormal sleep microstructure, are associated with mortality risk. These findings add to a growing literature suggesting that sleep contains incipient predictors of health and mortality.
Authors
- Katie L. Stone (ORCID: https://orcid.org/0000-0003-2797-3171)
- Andreas Brink-Kjær (ORCID: https://orcid.org/0000-0002-2872-0929)
- Poul Jennum (ORCID: https://orcid.org/0000-0001-6986-5254)
- Helge B. D. Sørensen (ORCID: https://orcid.org/0000-0001-5716-7600)
- Emmanuel Mignot (ORCID: https://orcid.org/0000-0002-6928-5310)
- Teitur Óli Kristjánsson (ORCID: https://orcid.org/0000-0002-0685-6809)
Institutions
- Palo Alto University (US)
- University of California, San Francisco (US)
- California Pacific Medical Center (US)
- Rigshospitalet (DK)
- Technical University of Denmark (DK)
- Stanford University (US)
Publication Details
- Journal
- SLEEP
- Published
- 2024-09-20
- DOI
- https://doi.org/10.1093/sleep/zsae219
- Citations
- 3
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 1.03
Funders
- Eli Lilly and Company
- University of Minnesota
- University of Washington
- Johns Hopkins University
- Case Western Reserve University
- Innovationsfonden
- York University
- National Institutes of Health
- University of California, Davis
- National Institute on Aging
- National Heart, Lung, and Blood Institute
- National Institute of Arthritis and Musculoskeletal and Skin Diseases