文献类型：期刊文献

英文题名：Analysis and Recognition of Traditional Chinese Medicine Pulse Based on the Hilbert-Huang Transform and Random Forest in Patients with Coronary Heart Disease

作者：Guo, Rui[1,2];Wang, Yiqin[1];Yan, Hanxia[1];Yan, Jianjun[3];Yuan, Fengyin[4];Xu, Zhaoxia[1];Liu, Guoping[1];Xu, Wenjie[1]

机构：[1]Shanghai Univ Tradit Chinese Med, Lab Informat Access & Synth TCM Diag 4, Shanghai 201203, Peoples R China;[2]Shanghai Univ Tradit Chinese Med, Ctr TCM Informat Sci & Technol, Shanghai 201203, Peoples R China;[3]E China Univ Sci & Technol, Ctr Mechatron Engn, Shanghai 200237, Peoples R China;[4]GDPU, Sch Clin Med, Affiliated Hosp 1, Guangzhou 51000, Guangdong, Peoples R China

年份：2015

卷号：2015

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

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000356832000001)】；

基金：This work was supported by National Natural Science Foundation of China for Youth under Grant no. 81302913 and National Natural Science Foundation of China under Grant nos. 81173199 and 81473594.

语种：英文

摘要：Objective. This research provides objective and quantitative parameters of the traditional Chinese medicine (TCM) pulse conditions for distinguishing between patients with the coronary heart disease (CHD) and normal people by using the proposed classification approach based on Hilbert-Huang transform(HHT) and random forest. Methods. The energy and the sample entropy features were extracted by applying the HHT to TCM pulse by treating these pulse signals as time series. By using the random forest classifier, the extracted two types of features and their combination were, respectively, used as input data to establish classification model. Results. Statistical results showed that there were significant differences in the pulse energy and sample entropy between the CHD group and the normal group. Moreover, the energy features, sample entropy features, and their combination were inputted as pulse feature vectors; the corresponding average recognition rates were 84%, 76.35%, and 90.21%, respectively. Conclusion. The proposed approach could be appropriately used to analyze pulses of patients with CHD, which can lay a foundation for research on objective and quantitative criteria on disease diagnosis or Zheng differentiation.