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
Article Authors Cited by Tweetations Metrics

Hong Wang   1, 2 , MM ;   Binbin Liu   1, 2 , MM ;   Hui Zhang   1 , MD ;   Zheqi Zhang   1, 2 , MM ;   Zhigeng Jin   1 , MD ;   Hao Wang   3 , Prof Dr ;   Yu-Tao Guo   1 , Prof Dr

1 Department of Pulmonary Vessel and Thrombotic Disease, Sixth Medical Center, Chinese PLA General Hospital, Beijing, China

2 Graduate School of PLA General Hospital, Beijing, China

3 Department of Cardiology, Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China

Corresponding Author:

  • Yu-Tao Guo, Prof Dr
  • Department of Pulmonary Vessel and Thrombotic Disease
  • Sixth Medical Center, Chinese PLA General Hospital
  • 6 Fucheng Road, Haidian District
  • Beijing 100048
  • China
  • Phone: +86-10-66957703
  • Email: dor_guoyt@hotmail.com