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听见:通过 IR-UWB 雷达进行带身体运动补偿的心搏监测方法。

HEAR: Approach for Heartbeat Monitoring with Body Movement Compensation by IR-UWB Radar.

机构信息

School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Xitucheng Road No.10, Beijing 100876, China.

School of Computer Science, Beijing University of Posts and Telecommunications, Xitucheng Road No.10, Beijing 100876, China.

出版信息

Sensors (Basel). 2018 Sep 13;18(9):3077. doi: 10.3390/s18093077.

Abstract

Further applications of impulse radio ultra-wideband radar in mobile health are hindered by the difficulty in extracting such vital signals as heartbeats from moving targets. Although the empirical mode decomposition based method is applied in recovering waveforms of heartbeats and estimating heart rates, the instantaneous heart rate is not achievable. This paper proposes a Heartbeat Estimation And Recovery (HEAR) approach to expand the application to mobile scenarios and extract instantaneous heartbeats. Firstly, the HEAR approach acquires vital signals by mapping maximum echo amplitudes to the fast time delay and compensating large body movements. Secondly, HEAR adopts the variational nonlinear chirp mode decomposition in extracting instantaneous frequencies of heartbeats. Thirdly, HEAR extends the clutter removal method based on the wavelet decomposition with a two-parameter exponential threshold. Compared to heart rates simultaneously collected by electrocardiograms (ECG), HEAR achieves a minimum error rate 4.6% in moving state and 2.25% in resting state. The Bland⁻Altman analysis verifies the consistency of beat-to-beat intervals in ECG and extracted heartbeat signals with the mean deviation smaller than 0.1 s. It indicates that HEAR is practical in offering clinical diagnoses such as the heart rate variability analysis in mobile monitoring.

摘要

脉冲无线电超宽带雷达在移动健康领域的进一步应用受到了从移动目标中提取心跳等重要信号的困难的阻碍。虽然经验模态分解方法被应用于恢复心跳的波形和估计心率,但无法实现瞬时心率。本文提出了一种 Heartbeat Estimation And Recovery(HEAR)方法,以扩展其在移动场景中的应用并提取瞬时心跳。首先,HEAR 方法通过将最大回波幅度映射到快速时延迟并补偿大的身体运动来获取重要信号。其次,HEAR 采用变分非线性啁啾模式分解来提取心跳的瞬时频率。第三,HEAR 将基于小波分解的杂波去除方法扩展为双参数指数阈值。与同时由心电图(ECG)收集的心率相比,HEAR 在移动状态下的最小误差率为 4.6%,在静止状态下的最小误差率为 2.25%。Bland⁻Altman 分析验证了 ECG 和提取的心跳信号之间的逐拍间隔的一致性,其平均偏差小于 0.1 秒。这表明 HEAR 在移动监测中的心率变异性分析等临床诊断中具有实用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/52ff/6165065/6f51b14820c8/sensors-18-03077-g001.jpg

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