Xu Huaxing, Wang Qia, Mao Xiaobo, Shang Zhigang, Zhao Yuping, Huang Luqi
School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, Henan, China.
Institute of Quantitative and Technological Economics, Chinese Academy of Social Sciences, Beijing 100732, China.
Evid Based Complement Alternat Med. 2022 Feb 8;2022:7285312. doi: 10.1155/2022/7285312. eCollection 2022.
Pulse signal analysis plays an important role in promoting the objectification of traditional Chinese medicine (TCM). Like electrocardiogram (ECG) signals, wrist pulse signals are mainly caused by cardiac activities and are valuable in analyzing cardiac diseases. A large number of studies have reported ECG signals can distinguish gender characteristics of normal healthy subjects using entropy complexity measures, consistently showing more complexity in females than males. No research up to date, however, has been found on examining gender differences with wrist pulse signals of healthy subjects on entropy complexity measures. This paper is aimed to fill in the research gap, which could, in turn, provide a deeper insight into the pulse signal and might identify potential differences between ECG signals and pulse signals. In particular, several complementary entropy measures with corresponding refined composite multiscale versions are established to perform the analysis for the filtered TCM pulse signals. Experimental results reveal that regardless of entropy measures used, there is no statistically significant gender difference in terms of entropy complexity, indicating that the pulse signal holds less gender characteristics than the ECG signal. In view of these findings, wrist pulse signals could be likely to provide different pieces of information to ECG signals. The present study is the first to quantitatively evaluate gender differences in healthy pulse signals with measures of entropy complexity and more importantly; we expect this study could make contribution to the ongoing pulse intelligent diagnosis and objective analysis, further facilitating the modernization of TCM pulse diagnosis.
脉搏信号分析在推动中医客观化方面发挥着重要作用。与心电图（ECG）信号一样，腕部脉搏信号主要由心脏活动引起，在分析心脏疾病方面具有重要价值。大量研究报告称，心电图信号可利用熵复杂度度量来区分正常健康受试者的性别特征，结果一致显示女性的复杂度高于男性。然而，目前尚未发现有研究使用熵复杂度度量来检验健康受试者腕部脉搏信号的性别差异。本文旨在填补这一研究空白，这反过来可能会为脉搏信号提供更深入的见解，并可能识别出心电图信号和脉搏信号之间的潜在差异。特别是，建立了几种互补的熵度量及其相应的精细化复合多尺度版本，以对滤波后的中医脉搏信号进行分析。实验结果表明，无论使用何种熵度量，在熵复杂度方面均不存在统计学上显著的性别差异，这表明脉搏信号所具有的性别特征比心电图信号少。鉴于这些发现，腕部脉搏信号可能会提供与心电图信号不同的信息。本研究首次使用熵复杂度度量对健康脉搏信号的性别差异进行定量评估，更重要的是，我们期望这项研究能够为正在进行的脉搏智能诊断和客观分析做出贡献，进一步推动中医脉搏诊断的现代化。
