Extending Multiscale Characterization of Heart Rate Variability via Deep Learning for Mortality Risk Prediction
This study highlights the potential of DMA and CNNs in improving mortality risk stratification and providing mechanistic insights into HRV dynamics, with implications for personalized health monitoring.
Authors
- Yasunari Fujimoto
- Ken Kiyono (ORCID: https://orcid.org/0000-0001-5433-7002)
- Eiichi Watanabe (ORCID: https://orcid.org/0000-0002-2375-2323)
- Sinyoung Lee (ORCID: https://orcid.org/0009-0003-1861-2881)
- João Gabriel Segato Kruse (ORCID: https://orcid.org/0000-0001-7096-4727)
Institutions
- Fujita Health University (JP)
- The University of Osaka (JP)
Publication Details
- Journal
- IEEE Transactions on Biomedical Engineering
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1109/tbme.2025.3614714
- Primary Topic
- Heart Rate Variability and Autonomic Control
- Type
- article
- Field-Weighted Citation Impact
- 0.00