Predicting sleep quality with digital biomarkers and artificial neural networks
This work introduces a novel approach for managing sleep health through continuous HRV monitoring and predictive modeling using wearable devices. The findings highlight the potential of the LF/HF ratio as a digital biomarker for sleep quality prediction, offering promise for personalized, data-driven healthcare interventions. The superior performance of deep learning methods underscores the value of temporal pattern recognition in sleep quality assessment, paving the way for proactive sleep health management in everyday life.
Authors
- Sungkyu Park (ORCID: https://orcid.org/0000-0002-2607-2120)
- Hansol Lee (ORCID: https://orcid.org/0000-0002-7950-6244)
- Sang‐Won Lee (ORCID: https://orcid.org/0000-0002-8038-3341)
- Minsung Cho
Institutions
- Korea Development Institute (KR)
- Kangwon National University (KR)
- Kyungpook National University Hospital (KR)
- Asan Medical Center (KR)
- Kyungpook National University (KR)
Publication Details
- Journal
- Frontiers in Psychiatry
- Published
- 2025-07-16
- DOI
- https://doi.org/10.3389/fpsyt.2025.1591448
- Citations
- 2
- Primary Topic
- Sleep and related disorders
- Type
- article
- Field-Weighted Citation Impact
- 4.24
Funders
- Kangwon National University
- Ministry of Health and Welfare
- Korea Health Industry Development Institute
- National Institutes of Health
- Korea National Institute of Health