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

Cardiac Arrhythmia, Atrial Fibrillation (117) Wearable Devices and Sensors (1256) Machine Learning (2323) Novel Sensors and Data Acquisition Methods in Cardiology (239) Mobile Apps for Cardiology (197) e-Cardiology and Telemonitoring in Cardiology (239) Cardiac Disease Management (168)

Published on in Vol 9 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/78075, first published .
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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 (2) Tweetations Metrics

Conference Proceedings

  1. Liao X, Li T, Luo J, Niramitchainont P, Li Y. 2026 6th International Conference on Consumer Electronics and Computer Engineering (ICCECE). Edge-AI Enabled Sustainable Consumer Electronics for Remote Health Monitoring View
  2. Atiq A, Masood S. 2026 Second International Conference on Emerging Computational Intelligence (ICECI). Early Atrial Fibrillation Diagnosis from Raw ECG Using a Conv1D–BiLSTM–Attention Deep Learning Framework View