School of Biomedical Engineering and Imaging Medicine, Army Medical University (Third Military Medical University), Chongqing 400038, China.
College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
Sensors (Basel). 2020 Aug 31;20(17):4913. doi: 10.3390/s20174913.
The non-contact monitoring of vital signs by radar has great prospects in clinical monitoring. However, the accuracy of separated respiratory and heartbeat signals has not satisfied the clinical limits of agreement. This paper presents a study for automated separation of respiratory and heartbeat signals based on empirical wavelet transform (EWT) for multiple people. The initial boundary of the EWT was set according to the limited prior information of vital signs. Using the initial boundary, empirical wavelets with a tight frame were constructed to adaptively separate the respiratory signal, the heartbeat signal and interference due to unconscious body movement. To verify the validity of the proposed method, the vital signs of three volunteers were simultaneously measured by a stepped-frequency continuous wave ultra-wideband (UWB) radar and contact physiological sensors. Compared with the vital signs from contact sensors, the proposed method can separate the respiratory and heartbeat signals among multiple people and obtain the precise rate that satisfies clinical monitoring requirements using a UWB radar. The detection errors of respiratory and heartbeat rates by the proposed method were within ±0.3 bpm and ±2 bpm, respectively, which are much smaller than those obtained by the bandpass filtering, empirical mode decomposition (EMD) and wavelet transform (WT) methods. The proposed method is unsupervised and does not require reference signals. Moreover, the proposed method can obtain accurate respiratory and heartbeat signal rates even when the persons unconsciously move their bodies.
雷达的非接触式生命体征监测在临床监测中有广阔的应用前景。然而，分离呼吸和心跳信号的准确性尚未满足临床协议的要求。本文提出了一种基于经验小波变换（EWT）的多人生理信号自动分离方法。EWT 的初始边界根据生命体征的有限先验信息设定。利用初始边界，构建具有紧框架的经验小波，自适应地分离呼吸信号、心跳信号和无意识身体运动引起的干扰。为了验证所提出方法的有效性，使用步进频率连续波超宽带（UWB）雷达和接触式生理传感器同时测量了三位志愿者的生命体征。与接触式传感器的生命体征相比，所提出的方法可以在 UWB 雷达中分离多人生理信号，获得满足临床监测要求的精确率。所提出方法的呼吸率和心率检测误差分别在±0.3 bpm 和±2 bpm 以内，远小于带通滤波、经验模态分解（EMD）和小波变换（WT）方法的检测误差。所提出的方法是无监督的，不需要参考信号。此外，即使被测人员无意识地移动身体，该方法也能获得准确的呼吸和心跳信号率。
