Atrial fibrillation detection via contactless radio monitoring and knowledge transfer.

Journal: Nature communications
Published Date: May 20, 2025

Abstract

Atrial fibrillation (AF) has been a prevalent and serious arrhythmia associated with increased morbidity and mortality worldwide. The Electrocardiogram (ECG) is considered as the golden standard for AF diagnosis. However, current ECG is primarily used only when symptoms arise or for occasional checkups due to the necessity of contact-based measurements. This limitation results in difficulty of capturing early-stage AF episodes and missed opportunities for timely intervention. Here we introduce a contactless, operation-free, and device-free AF detection framework utilizing artificial intelligence (AI)-powered radio technology. Our approach analyzes the mechanical motion of the heart using radar sensing and leverages AI-powered knowledge transfer from established clinical ECG diagnostic practices to read AF-associated motion patterns precisely. Our system is evaluated on 6258 outpatient visitors, including 229 with AF, and achieves AF detection with a sensitivity of 0.844 (95% Confidence Interval (CI), 0.790-0.884) and a specificity of 0.995 (95% CI, 0.993-0.997), which is comparable to the performance of ECG-based methods. We also provide initial evidence that this system could be deployed in a practical daily life scenario, detecting AF before traditional clinical diagnosis routines. These results highlight its potential to support feasible lifelong proactive monitoring, covering the full spectrum of AF progression.

Authors

  • Yuqin Yuan
    Department of Cardiology, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, Anhui, China.
  • Jinbo Chen
    Department of Urology, Xiangya Hospital, Central South University, Changsha 410008, China.
  • Dongheng Zhang
    School of Cyber Science and Technology, University of Science and Technology of China, Hefei, Anhui, China.
  • Ruixu Geng
    School of Cyber Science and Technology, University of Science and Technology of China, Hefei, Anhui, China.
  • Hanqin Gong
    School of Cyber Science and Technology, University of Science and Technology of China, Hefei, Anhui, China.
  • Guixin Xu
    School of Cyber Science and Technology, University of Science and Technology of China, Hefei, Anhui, China.
  • Yu Pu
    School of Cyber Science and Technology, University of Science and Technology of China, Hefei, Anhui, China.
  • Zhi Lu
    The University of South Australia, Australia.
  • Yang Hu
    Kweichow Moutai Co., Ltd, Renhuai, Guizhou 564501, China.
  • Dong Zhang
    Institute of Acoustics, Nanjing University, Nanjing 210093, China.
  • Likun Ma
    Department of Cardiology, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, Anhui, China.
  • Qibin Sun
    Zhongke Radio Sensing AI Technology Co., Ltd., Hefei, Anhui, China.
  • Yan Chen
    Department of Respiratory and Critical Care Medicine, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China.

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