Detecting Non-Stationarity for Auscultation Signal of Traditional Chinese Medicine  

作　　者：YAN Jianjun SHEN Qingwei WANG Yiqin LI Fufeng GUO Rui CHEN Chunfeng SHEN Yong 

机构地区：[1]Center for Mechatronics Engineering, East China Universityof Science and Technology, Shanghai 200237, China [2]Center for Traditional Chinese Medicine InformationScience and Technology, Shanghai University of TraditionalChinese Medicine, Shanghai 201203, China

出　　处：《Wuhan University Journal of Natural Sciences》2011年第1期83-87,共5页武汉大学学报（自然科学英文版）

基　　金：Supported by the National Natural Science Foundation of China (30701072);Supported by the National Science and Technology Support-ing Program in the Eleventh Five-Year Plan of China (2006BAI08B01-04);Construction Fund for Key Subjects of Shanghai (S30302)

摘　　要：The information about the nonstationarity of the aus-cultation signal is utilized in this paper to objectively and auto-matically identify healthy people and patients with qi-deficiency or yin-deficiency. In order to characterize the nonstationarity of the sound signal,the nonlinear cross-prediction method is used to extract features from the signal. A feature selection method based on conditional mutual information maximization criterion （CMIM） is implemented to find an optimal feature set. By means of the support vector machine （SVM） classifier,three common states （healthy,qi-deficiency and yin-deficiency） in traditional Chinese medicine are distinguished using the feature set,and a satisfactory classification accuracy of 80% is achieved in the experiment. In conclusion,the analysis based on the nonstationarity of the sound signal provides an alternative and outstanding approach to the objective auscultation of traditional Chinese medicine （TCM）.The information about the nonstationarity of the aus-cultation signal is utilized in this paper to objectively and auto-matically identify healthy people and patients with qi-deficiency or yin-deficiency. In order to characterize the nonstationarity of the sound signal,the nonlinear cross-prediction method is used to extract features from the signal. A feature selection method based on conditional mutual information maximization criterion （CMIM） is implemented to find an optimal feature set. By means of the support vector machine （SVM） classifier,three common states （healthy,qi-deficiency and yin-deficiency） in traditional Chinese medicine are distinguished using the feature set,and a satisfactory classification accuracy of 80% is achieved in the experiment. In conclusion,the analysis based on the nonstationarity of the sound signal provides an alternative and outstanding approach to the objective auscultation of traditional Chinese medicine （TCM）.

关 键 词：AUSCULTATION nonstationarity support vector ma-chine （SVM） traditional Chinese medicine （TCM） 

分 类 号：R318[医药卫生—生物医学工程]