SYSTEMATIC REVIEW article

Front. Cardiovasc. Med.

Sec. Cardiac Rhythmology

Volume 12 - 2025 | doi: 10.3389/fcvm.2025.1581683

This article is part of the Research TopicArtificial Intelligence for Arrhythmia Detection and PredictionView all 8 articles

Identification of Atrial Fibrillation Using Heart Rate Variability: A Meta-analysis

Provisionally accepted
Ziwei  YinZiwei Yin1Changxin  LiuChangxin Liu1Chenggong  XieChenggong Xie1Zinxing  NieZinxing Nie2Jiaming  WeiJiaming Wei1Wen  ZhangWen Zhang2*Hao  LiangHao Liang1*
  • 1Hunan University of Chinese Medicine, Changsha, China
  • 2The First Hospital of Hunan University of Chinese Medicine, Changsha, Hunan, China

The final, formatted version of the article will be published soon.

Background: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and is associated with significant cardiovascular complications. Recently, artificial intelligence (AI) algorithms have leveraged heart rate variability (HRV) patterns to enhance the accuracy of AF identification. Methods: We conducted a systematic review of the literature by searching four major biomedical databases-PubMed, Web of Science, Embase, and Cochrane Library-spanning from their inception to December 13, 2024, following the PRISMA guidelines. We extracted data on true positives, false positives, true negatives, and false negatives from the included studies, which were then synthesized to evaluate sensitivity and specificity comprehensively. Results: Our final analysis included 12 diagnostic studies. Hierarchical summary receiver operating characteristic modeling revealed excellent discriminative ability, with a pooled sensitivity of 0.94 and specificity of 0.97. In detecting AF, the AI model demonstrated exceptional performance (sensitivity = 0.96, specificity = 0.99, AUC = 1.00). Subgroup analyses revealed that both deep learning algorithms (sensitivity = 0.95, specificity = 0.98, AUC = 0.99) and multi-database studies (sensitivity = 0.96, specificity = 0.97, AUC = 0.99) demonstrated enhanced accuracy in AF identification compared to other approaches. Conclusion: Machine learning can effectively identify AF with HRV in ECG, especially in diagnosis and detection, with deep learning algorithms and multiple-databases outperforming other diagnostic methods.

Keywords: deep learning, machine learning, Atrial Fibrillation, Heart rate variability, ECG, Meta-analysis

Received: 22 Feb 2025; Accepted: 02 Jun 2025.

Copyright: © 2025 Yin, Liu, Xie, Nie, Wei, Zhang and Liang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Wen Zhang, The First Hospital of Hunan University of Chinese Medicine, Changsha, Hunan, China
Hao Liang, Hunan University of Chinese Medicine, Changsha, China

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.