Application of voice signal collection and analysis in traditional Chinese medicine syndrome differentiation of deficiency and excess

Abstract

To provide more objective basis for traditional Chinese medicine (TCM) syndrome differentiation of deficiency and excess by collecting and analyzing voice signals and extracting characteristic parameters. All of 308 samples including 150 samples of qi-deficiency, 52 yin-deficiency, 55 excess and 51 normal were collected by "Voice Collecting System of TCM" and analyzed by wavelet packet transform (WPT) combined with approximate entropy (ApEn). The characteristic parameters with remarkable differences were chosen as input vectors for support vector machine (SVM) classifier to obtain classification results. Comparison between the normal and non-healthy showed that ApEn values of several nodes in 2 to 3 kHz, 3.5 to 4 kHz and 5 to 8 kHz frequencies were significantly different (P≤0.05); comparison between the deficiency and excess showed that ApEn values of several nodes in 0 to 2.5 kHz and 6 to 6.5 kHz frequencies were significantly different (P≤0.05); comparison between the qi-deficiency and yin-deficiency showed that ApEn values of node in 2 to 4 kHz frequency were significantly different(P≤0.05). The outputs of SVM showed that accuracies of samples in each group had good classification results by analyzing the ApEn values in different frequencies through WPT. The methods of voice collection and analysis used in the auscultation of TCM can provide objective basis for syndrome differentiation of deficiency and excess.