文献类型：期刊文献

英文题名：Auscultation signals analysis in traditional Chinese medicine using wavelet packet energy entropy and support vector machines

作者：Yan, Jianjun[1]; Shen, Xiaojing[1]; Xia, Chunming[1]; Shen, Yong[1]; Gu, Zhong Yan[1]; Wang, Yiqin[2]; Li, Fufeng[2]; Guo, Rui[2]; Chen, Chunfeng[2]; Chen, Lingyun[2]; Yan, Bin[2]

机构：[1] Center for Mechatronics Engineering, East China University of Science and Technology, Shanghai 200237, China; [2] Center for TCM information science and Technology, Shanghai University of TCM, Shanghai 201203, China

年份：2010

起止页码：509

外文期刊名：Proceedings - International Conference on Electrical and Control Engineering, ICECE 2010

收录：EI(收录号：20111013722097)

语种：英文

外文关键词：Medicine - Entropy - Wavelet analysis - Wavelet decomposition

摘要：In this paper, wavelet packet energy entropy (WPEE) and support vector machine (SVM) were utilized to detect and classify auscultation signals in Traditional Chinese Medicine (TCM). The auscultation signals of health and qi-vacuity and yin-vacuity subjects were collected from the outpatient by Shanghai University of TCM. And the wavelet packet decomposition (WPD) at level 6 was employed to split more elaborate frequency bands of the auscultation signals, then to obtain energy entropies features of frequency bands. SVM are designed and trained for making a decision regarding the type of the auscultation signals. The experimental results showed the algorithm using WPEE and SVM classifier feasibility and effectiveness, and this paper is valuable for auscultation research in TCM. ? 2010 IEEE.