Monitoring long-term cardiac activity with contactless radio frequency signals

Bin-Bin Zhang, Dongheng Zhang, Yadong Li, Zhi Lu, Jinbo Chen, Haoyu Wang, Fang Zhou, Yu Pu, Yang Hu, Li-Kun Ma, Qibin Sun and Yan Chen ()
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Bin-Bin Zhang: 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
Yan Chen: University of Science and Technology of China

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-55061-9

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