Nonlinear Analysis of Auscultation Signals in Traditional Chinese Medicine Using Wavelet Transform and Approximate Entropy

The purpose of this paper is to analyze the auscultation signals in Traditional Chinese Medicine utilizing discrete wavelet (DWT) and approximate entropy (ApEn). In this paper ApEn is used to quantify pathological voicing in qi-deficiency, yin-deficiency and health using the voice samples in the time and frequency domain. Since ApEn is a viable single figure of merit, it has the potential to make assessment of aberrant voicing both more concise and objective than the subjective analysis adopted by speech and language therapists (SALTs). In this paper, in the first stage, voice signal were decomposed into approximation and detail coefficients using DWT. In the second stage, ApEn values of approximation and detail coefficients were computed. Finally, ApEn values among three kinds of samples were analyzed.