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 ...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).展开更多
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)
文摘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).
