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利用非接触式射频信号监测长期心脏活动。

Monitoring long-term cardiac activity with contactless radio frequency signals.

作者信息

Zhang Bin-Bin, Zhang Dongheng, Li Yadong, Lu Zhi, Chen Jinbo, Wang Haoyu, Zhou Fang, Pu Yu, Hu Yang, Ma Li-Kun, Sun Qibin, Chen Yan

机构信息

School of Cyber Science and Technology, University of Science and Technology of China, Hefei, China.

The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, China.

出版信息

Nat Commun. 2024 Dec 5;15(1):10598. doi: 10.1038/s41467-024-55061-9.

DOI:10.1038/s41467-024-55061-9
PMID:39638816
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11621424/
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.

摘要

心血管疾病每年导致超过1000万人死亡,这凸显了对心脏异常进行长期监测和早期检测的迫切需求。现有的技术,如心电图(ECG)和动态心电图,虽然准确,但存在因身体附着电极带来的不适。虽然使用光电容积脉搏波描记法的可穿戴设备提供了更多便利,但它们牺牲了准确性,并且容易受到环境干扰。在此,我们展示了一种基于射频(RF)(60至64吉赫兹)的传感系统,该系统能够以临床级精度监测长期心率变异性(HRV)。我们的系统成功克服了远场条件下呼吸运动带来的大得多的干扰,无需任何模型训练。通过识别心跳信息在其中比其他运动占主导地位的先前未被发现的频率范围(超过10阶心跳谐波),我们生成了通常被认为有害的带有谐波的显著心跳模式。广泛的评估,包括涉及6222名合格参与者的大规模门诊环境以及长期日常生活场景(在两个月内分5个独立随机夜晚以及连续21个夜晚收集睡眠数据),表明我们的系统能够监测HRV并识别异常,其性能与基于临床级ECG的系统相当。这种基于RF的HRV传感系统有潜力支持主动自我评估,并通过长期精确的健康监测彻底改变医疗预防。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e281/11621424/8602bc092bc9/41467_2024_55061_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e281/11621424/519fd634bc9a/41467_2024_55061_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e281/11621424/09a41112fbab/41467_2024_55061_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e281/11621424/8602bc092bc9/41467_2024_55061_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e281/11621424/519fd634bc9a/41467_2024_55061_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e281/11621424/d94ac9e1edf9/41467_2024_55061_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e281/11621424/1daef28c2115/41467_2024_55061_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e281/11621424/09a41112fbab/41467_2024_55061_Fig4_HTML.jpg
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Psychosom Med. 2022;84(2):210-214. doi: 10.1097/PSY.0000000000001010.
4
Accurate Heart Rate and Respiration Rate Detection Based on a Higher-Order Harmonics Peak Selection Method Using Radar Non-Contact Sensors.基于使用雷达非接触式传感器的高阶谐波峰值选择方法的精确心率和呼吸率检测
Sensors (Basel). 2021 Dec 23;22(1):83. doi: 10.3390/s22010083.
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6
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7
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