Monitoring long-term cardiac activity with contactless radio frequency signals
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 el...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-55061-9 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Bin-Bin Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1f5ebebcf361494ea8d42b628c7de5b2 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-1f5ebebcf361494ea8d42b628c7de5b22024-12-08T12:37:01ZengNature PortfolioNature Communications2041-17232024-12-0115111110.1038/s41467-024-55061-9Monitoring long-term cardiac activity with contactless radio frequency signalsBin-Bin Zhang0Dongheng Zhang1Yadong Li2Zhi Lu3Jinbo Chen4Haoyu Wang5Fang Zhou6Yu Pu7Yang Hu8Li-Kun Ma9Qibin Sun10Yan Chen11School of Cyber Science and Technology, University of Science and Technology of ChinaSchool of Cyber Science and Technology, University of Science and Technology of ChinaDepartment of Electrical and Computer Engineering, University of WashingtonSchool of Cyber Science and Technology, University of Science and Technology of ChinaSchool of Cyber Science and Technology, University of Science and Technology of ChinaSchool of Cyber Science and Technology, University of Science and Technology of ChinaSchool of Cyber Science and Technology, University of Science and Technology of ChinaSchool of Cyber Science and Technology, University of Science and Technology of ChinaSchool of Cyber Science and Technology, University of Science and Technology of ChinaThe First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of ChinaSchool of Cyber Science and Technology, University of Science and Technology of ChinaSchool of Cyber Science and Technology, University of Science and Technology of ChinaAbstract 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.https://doi.org/10.1038/s41467-024-55061-9 |
| spellingShingle | 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 Monitoring long-term cardiac activity with contactless radio frequency signals Nature Communications |
| title | Monitoring long-term cardiac activity with contactless radio frequency signals |
| title_full | Monitoring long-term cardiac activity with contactless radio frequency signals |
| title_fullStr | Monitoring long-term cardiac activity with contactless radio frequency signals |
| title_full_unstemmed | Monitoring long-term cardiac activity with contactless radio frequency signals |
| title_short | Monitoring long-term cardiac activity with contactless radio frequency signals |
| title_sort | monitoring long term cardiac activity with contactless radio frequency signals |
| url | https://doi.org/10.1038/s41467-024-55061-9 |
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