文献类型：期刊文献

英文题名：Nonlinear Analysis of Auscultation Signals in TCM Using the Combination of Wavelet Packet Transform and Sample Entropy

作者：Yan, Jian-Jun[1];Wang, Yi-Qin[2];Guo, Rui[2];Zhou, Jin-Zhuan[1];Yan, Hai-Xia[2];Xia, Chun-Ming[1];Shen, Yong[1]

机构：[1]E China Univ Sci & Technol, Ctr Mechatron Engn, Shanghai 200237, Peoples R China;[2]Shanghai Univ Tradit Chinese Med, Syndrome Lab TCM, Shanghai 201203, Peoples R China

年份：2012

卷号：2012

外文期刊名：EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000304983500001)】；

基金：This work was supported by the National Natural Science Foundation of China (Grants no. 30701072, 81173199, and 30901897) and the Shanghai 3rd Leading Academic Discipline Project (Grant no. S30302).

语种：英文

摘要：Auscultation signals are nonstationary in nature. Wavelet packet transform (WPT) has currently become a very useful tool in analyzing nonstationary signals. Sample entropy (SampEn) has recently been proposed to act as a measurement for quantifying regularity and complexity of time series data. WPT and SampEn were combined in this paper to analyze auscultation signals in traditional Chinese medicine (TCM). SampEns for WPT coefficients were computed to quantify the signals from qi- and yin-deficient, as well as healthy, subjects. The complexity of the signal can be evaluated with this scheme in different time-frequency resolutions. First, the voice signals were decomposed into approximated and detailed WPT coefficients. Then, SampEn values for approximated and detailed coefficients were calculated. Finally, SampEn values with significant differences in the three kinds of samples were chosen as the feature parameters for the support vector machine to identify the three types of auscultation signals. The recognition accuracy rates were higher than 90%.