  Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with
significant health ramifications, including an elevated susceptibility to
ischemic stroke, heart disease, and heightened mortality. Photoplethysmography
(PPG) has emerged as a promising technology for continuous AF monitoring for
its cost-effectiveness and widespread integration into wearable devices. Our
team previously conducted an exhaustive review on PPG-based AF detection before
June 2019. However, since then, more advanced technologies have emerged in this
field. This paper offers a comprehensive review of the latest advancements in
PPG-based AF detection, utilizing digital health and artificial intelligence
(AI) solutions, within the timeframe spanning from July 2019 to December 2022.
Through extensive exploration of scientific databases, we have identified 59
pertinent studies. Our comprehensive review encompasses an in-depth assessment
of the statistical methodologies, traditional machine learning techniques, and
deep learning approaches employed in these studies. In addition, we address the
challenges encountered in the domain of PPG-based AF detection. Furthermore, we
maintain a dedicated website to curate the latest research in this area, with
regular updates on a regular basis.
