Nonlinear analysis of auscultation signals in Traditional Chinese Medicine using Wavelet Packet Transform and Approximate Entropy

The distinctive characteristic of Wavelet Transform (WT) is that it can well characterise the local information of signals in time-frequency domain, and Wavelet Packet Transform (WPT) has a more subtle decomposition method than WT. The purpose of this paper is to analyse the auscultation signals in Traditional Chinese Medicine (TCM) utilising WPT and Approximate Entropy (ApEn). In this paper, a new scheme was presented for analysing the Auscultation Signals consisted of qi-deficient, yin-deficient and normal people. In the first stage, voice signal were decomposed into approximation and detail coefficients using WPT. Then the ApEn values of these signals were computed based on these coefficients. The differences of the ApEn values and the meaning of which for signals among three kinds of samples were discussed. Finally the conclusion can be drawn that the distributions of ApEn in different frequency ranges for all signals of three kinds of samples have their special characteristics. The ApEn values for three kinds of samples were used as the feature vectors for Support Vector Machine (SVM) classifier and some impressing results of classifications can be obtained.