Photoplethysmography-Based Machine Learning Approaches for Atrial Fibrillation Burden: Algorithm Development and Validation
The PPG-based AF burden model demonstrated high concordance with the gold standard of 24-hour Holter monitoring in tracking AF episode duration and variability, providing new perspectives for exploring AF progression dynamics.
Authors
- Yutao Guo (ORCID: https://orcid.org/0000-0002-7626-7751)
- B.X. Liu (ORCID: https://orcid.org/0009-0002-1907-2875)
- Zhigeng Jin (ORCID: https://orcid.org/0000-0003-2971-6697)
- Zheqi Zhang (ORCID: https://orcid.org/0009-0009-0299-2561)
- Hui Zhang (ORCID: https://orcid.org/0000-0003-1942-2513)
- Hong Wang (ORCID: https://orcid.org/0000-0002-6697-5172)
- Hao Wang (ORCID: https://orcid.org/0000-0003-4082-9350)
Institutions
- Chinese PLA General Hospital (CN)
Publication Details
- Journal
- JMIR Cardio
- Published
- 2025-11-10
- DOI
- https://doi.org/10.2196/78075
- Citations
- 1
- Primary Topic
- Non-Invasive Vital Sign Monitoring
- Type
- article
- Field-Weighted Citation Impact
- 0.67
Funders
- National Natural Science Foundation of China