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Monitoring long-term cardiac activity with contactless radio frequency signals

Author

Listed:
  • Bin-Bin Zhang

    (University of Science and Technology of China
    University of Science and Technology of China)

  • Dongheng Zhang

    (University of Science and Technology of China)

  • Yadong Li

    (University of Washington)

  • Zhi Lu

    (University of Science and Technology of China)

  • Jinbo Chen

    (University of Science and Technology of China)

  • Haoyu Wang

    (University of Science and Technology of China)

  • Fang Zhou

    (University of Science and Technology of China)

  • Yu Pu

    (University of Science and Technology of China)

  • Yang Hu

    (University of Science and Technology of China)

  • Li-Kun Ma

    (University of Science and Technology of China)

  • Qibin Sun

    (University of Science and Technology of China
    Ltd)

  • Yan Chen

    (University of Science and Technology of China
    University of Science and Technology of China)

Abstract

Cardiovascular diseases claim over 10 million lives annually, highlighting the critical need for long-term monitoring and early detection of cardiac abnormalities. Existing techniques like electrocardiograms (ECG) and Holter are accurate but suffer from discomfort caused by body-attached electrodes. While wearable devices using photoplethysmography offer more convenience, they sacrifice accuracy and are susceptible to environmental interference. Here we present a radio frequency (RF)-based (60 to 64 GHz) sensing system that monitors long-term heart rate variability (HRV) with clinical-grade accuracy. Our system successfully overcomes the orders-larger interference from respiration motion in far-field conditions without any model training. By identifying previously undiscovered frequency ranges (beyond 10-order heartbeat harmonics) where heartbeat information predominates over other motions, we generate prominent heartbeat patterns with harmonics typically considered detrimental. Extensive evaluations, including a large-scale outpatient setting involving 6,222 eligible participants and a long-term daily life scenario, where sleep data was collected over 5 separate random nights over two months and a continuous 21-night period, demonstrate that our system can monitor HRV and identify abnormalities with comparable performance to clinical-grade ECG-based systems. This RF-based HRV sensing system has the potential to support active self-assessment and revolutionize medical prevention with long-term and precise health monitoring.

Suggested Citation

  • Bin-Bin Zhang & Dongheng Zhang & Yadong Li & Zhi Lu & Jinbo Chen & Haoyu Wang & Fang Zhou & Yu Pu & Yang Hu & Li-Kun Ma & Qibin Sun & Yan Chen, 2024. "Monitoring long-term cardiac activity with contactless radio frequency signals," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-55061-9
    DOI: 10.1038/s41467-024-55061-9
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    References listed on IDEAS

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    1. Antônio H. Ribeiro & Manoel Horta Ribeiro & Gabriela M. M. Paixão & Derick M. Oliveira & Paulo R. Gomes & Jéssica A. Canazart & Milton P. S. Ferreira & Carl R. Andersson & Peter W. Macfarlane & Wagner, 2020. "Automatic diagnosis of the 12-lead ECG using a deep neural network," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    2. Zhou, Feite & Huang, Zhehao & Zhang, Changhong, 2022. "Carbon price forecasting based on CEEMDAN and LSTM," Applied Energy, Elsevier, vol. 311(C).
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