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

机译:HEAR:通过IR-UWB雷达进行具有身体运动补偿的心跳监测方法

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摘要

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.
机译:脉冲无线电超宽带雷达在移动医疗中的进一步应用由于难以从移动目标中提取诸如心跳之类的重要信号而受到阻碍。尽管将基于经验模式分解的方法应用于恢复心跳的波形并估算心率,但无法实现瞬时心率。本文提出了一种心跳估计和恢复(HEAR)方法,以将应用程序扩展到移动方案并提取瞬时心跳。首先,HEAR方法通过将最大回波幅度映射到快速延时并补偿大型人体运动来获取重要信号。其次,HEAR在提取心跳瞬时频率时采用变分非线性nonlinear模式分解。第三,HEAR扩展了基于小波分解且具有两个参数指数阈值的​​杂波去除方法。与通过心电图(ECG)同时收集的心率相比,HEAR在运动状态下的最小错误率达到4.6%,在静止状态下的最小错误率达到2.25%。 Bland–Altman分析验证了心电图和提取的心跳信号之间心跳间隔的一致性,其平均偏差小于0.1 s。这表明HEAR在提供临床诊断(例如移动监控中的心率变异性分析)方面非常实用。

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