This paper is in the following e-collection/theme issue:

Cardiac Arrhythmia, Atrial Fibrillation (115) Machine Learning (2076) Wearable Devices and Sensors (1133) Cardiac Disease Management (156) e-Cardiology and Telemonitoring in Cardiology (221) Novel Sensors and Data Acquisition Methods in Cardiology (225) Mobile Apps for Cardiology (189)

Published on in Vol 9 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/78075, first published .
Photoplethysmography-Based Machine Learning Approaches for Atrial Fibrillation Burden: Algorithm Development and Validation

Photoplethysmography-Based Machine Learning Approaches for Atrial Fibrillation Burden: Algorithm Development and Validation

Photoplethysmography-Based Machine Learning Approaches for Atrial Fibrillation Burden: Algorithm Development and Validation

Authors of this article:

Hong Wang1, 2 Author Orcid Image ;   Binbin Liu1, 2 Author Orcid Image ;   Hui Zhang1 Author Orcid Image ;   Zheqi Zhang1, 2 Author Orcid Image ;   Zhigeng Jin1 Author Orcid Image ;   Hao Wang3 Author Orcid Image ;   Yu-Tao Guo1 Author Orcid Image
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