Noncontact Heart Rate Measurement Based on an Improved Convolutional Sparse Coding Method Using IR-UWB Radar
Noncontact Heart Rate Measurement Based on an Improved Convolutional Sparse Coding Method Using IR-UWB Radar
复制标题
基于改进的卷积稀疏编码方法的 IR-UWB 雷达非接触式心率测量
DOI：
10.1109/access.2019.2950423
复制
发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Wang, Jianqi
中科院分区：
文献类型：
--
作者：
Wang, Pengfei;Qi, Fugui;Wang, Jianqi
冲激无线电超宽带雷达是生命探测和生命信号非接触监测的重要遥感工具。在雷达非接触监测中，呼吸和环境噪声的干扰被认为是估计心率的关键。然而，心跳信号在时间域中通常会受到呼吸谐波和波动的影响，而生命信号的频率又很接近，因此很难使用普通的频率滤波器进行分离。为了解决这一问题，提出了一种新的心跳信息提取方法。在这项研究中，卷积稀疏编码是一种无监督的机器学习算法，在给定呼吸和相关伪影的情况下，首次使用卷积稀疏编码在时间域中对心跳信号建模。然后利用心跳信号在时间域中的稀疏性，对信号进行分解，直接得到心跳分量。此外，利用噪声辅助的方法提高了方案的性能，并通过学习K-奇异值分解策略加快了算法的执行速度。最后，通过对从时域有限差分仿真和实验中采集到的生命体征信号进行测试，结果表明，该方法能够有效地从呼吸信号中提取出低幅度的心跳信号，显著提高了心率估计的准确性。
Impulse radio ultrawideband radar is a critical remote sensing tool for life detection and noncontact monitoring of vital signals. In noncontact monitoring via radar, the disturbance from respiration and environmental noise is considered critical for the estimation of heart rates. However, the heartbeat signal is generally distorted by breath harmonics and fluctuations in the time domain, and the frequencies of the vital signals are closely situated; thus, it is difficult to employ an ordinary frequency filter for separation. To solve this problem, a novel method was developed to extract heartbeat information. In this study, convolutional sparse coding, which is an unsupervised machine learning algorithm, was first used to model the heartbeat signal in the time domain, given the respiration and relative artifacts. The proposed scheme was then used to decompose the time-domain signals and directly obtain the heartbeat component by exploiting the sparsity of the heartbeat signal in the time domain. Furthermore, the performance of the proposed scheme was improved using a noise-assisted method, and the process was accelerated by learning from the K-singular value decomposition strategy. Finally, by testing the vital sign signals collected from the finite differences time-domain simulation and experiments, the results obtained indicate that the proposed approach is effective for the extraction of low-amplitude heartbeat signals from the respiration signal, and that it significantly improves the accuracy of heart rate evaluation.