Pediatric SleepNet: A Deep Learning Network for Reliable Pediatric Sleep Staging Across Developmental Stages
pediatric SleepNet demonstrates reliable sleep staging across pediatric development. Its robust performance across age, disease, and external datasets supports its potential for clinical and research use in pediatric sleep medicine.
Authors
- Wolfgang Ganglberger (ORCID: https://orcid.org/0000-0002-6029-2450)
- Ayan Mitra (ORCID: https://orcid.org/0000-0002-9436-8871)
- R. Quinn Thomas (ORCID: https://orcid.org/0000-0003-1282-7825)
- Samuel T. Waters
- Lynn Marie Trotti (ORCID: https://orcid.org/0000-0003-2329-6847)
- Umakanth Katwa
- Gari D. Clifford (ORCID: https://orcid.org/0000-0002-5709-201X)
- Kiran Maski (ORCID: https://orcid.org/0000-0003-4073-4036)
- Matthew A. Reyna (ORCID: https://orcid.org/0000-0003-4688-7965)
- Dennis W. Hwang (ORCID: https://orcid.org/0000-0001-9974-4728)
- Arnav Gupta (ORCID: https://orcid.org/0009-0003-4818-2130)
- Haoqi Sun
- Emmanuel Mignot
- Samaneh Nasiri
- Ayush Tripathi
- Katie L Stone
- M Brandon Westover
Institutions
- Boston Children's Hospital (US)
- Georgia Institute of Technology (US)
- Beth Israel Deaconess Medical Center (US)
- Harvard University (US)
- Emory University (US)
- Palo Alto University (US)
- University of California, San Francisco (US)
- California Pacific Medical Center (US)
- Harvard University Press (US)
- Pacific Medical (China) (CN)
- San Bernardino County Department of Public Health (US)
- Boston Children's Museum (US)
- Stanford University (US)
Publication Details
- Journal
- SLEEP
- Published
- 2026-03-03
- DOI
- https://doi.org/10.1093/sleep/zsag064
- Primary Topic
- Sleep and related disorders
- Type
- article
- Field-Weighted Citation Impact
- 0.00