基于时变滤波经验模态分解的非接触式心率变异性估计 方法. (Chinese)

A Non‑contact HRV Estimation Method Based on TVF‑EMD. (English)

The physical health status of civil aviation personnel is an important factor affecting aviation safety, among which respiration and heart rate are extremely important indicators of health. To address the limitations and interference of contact or wearable measurement systems on personnel during working, linear frequency-modulated continuous wave (FMCW) radar can be used to achieve non-contact measurement. Since vital sign signals have the characteristics of time-varying and non-stationary, to solve the problem of mode aliasing in empirical mode decomposition (EMD) in signal decomposition, the timevarying filtering based on EMD (TVF-EMD) can adaptively adjust the local cutoff frequency of the signal, effectively improving the signal separation performance and solving the mode aliasing problem. By using the intrinsic mode functions (IMF) components decomposed by TVF-EMD to reconstruct the time domain signal corresponding to the heartbeat, the frequency and inter-beat interval (IBI) of the heartbeat signal can be estimated, and further the relevant indicators of heart rate variability (HRV) can be estimated. Simulation experiments and actual measured data processing results show that TVF-EMD can effectively separate respiration and heartbeat signals from millimeter wave radar measurement signals. At the same time, a simulation analysis of the decomposition effects of TVF-EMD and EMD methods from the aspects of mode aliasing degree and signal separation performance has been conducted, and the results show that TVF-EMD can effectively solve the mode aliasing problem. Therefore, the TVF-EMD method can accurately and effectively extract vital sign information from millimeter wave radar measurement signals, provide accurate time-domain information for IBI estimation and HRV analysis, and has a broad application prospect. [ABSTRACT FROM AUTHOR]

民航从业人员的身体健康状况是影响航空安全的重要因素，其中呼吸和心跳是极其重要的健 康状况表征。为解决接触式或穿戴式测量系统对人员工作时的局限与影响，可采用线性调频连续波 （Frequency‑modulated continuous wave，FMCW）雷达达到非接触式测量的目的。由于生命体征信号具 有时变、非平稳的特点，针对经验模态分解（Empirical mode decomposition，EMD）在信号分解中存在模 态混叠现象的问题，使用时变滤波经验模态分解（Time varying filtering based on EMD，TVF‑EMD）自 适应信号的局部截止频率，可有效提高信号分离性能，解决模态混叠问题。利用 TVF‑EMD 分解出的 本征模态函数（Intrinsic mode functions，IMF）分量重构心跳对应的时域信号，估计心跳信号的频率和心 跳节拍间隔（Inter‑beat interval，IBI），进一步对心率变异性（Heart rate variability， HRV）相关指标进行 估计。仿真实验与实测数据处理结果表明，TVF‑EMD 可从毫米波雷达测量信号中有效分离出呼吸与 心跳信号。同时，从模态混叠程度及信号分离性能两方面对 TVF‑EMD 与 EMD 方法分解效果进行了 仿真分析，结果表明 TVF‑EMD 能够有效解决模态混叠问题。因此，TVF‑EMD 方法能够准确有效地 从毫米波雷达测量信号中提取生命体征信息，为 IBI 估计和 HRV 分析提供准确的时域信息，具有广阔 的应用前景。 [ABSTRACT FROM AUTHOR]

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