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Evidence-Based Complementary and Alternative Medicine
Volume 2014 (2014), Article ID 502348, 11 pages
Research Article

Objective Auscultation of TCM Based on Wavelet Packet Fractal Dimension and Support Vector Machine

1Center for Mechatronics Engineering, East China University of Science and Technology, Shanghai 200237, China
2Laboratory of Information Access and Synthesis of TCM Four Diagnostic, Shanghai University of Chinese Traditional Medicine, Shanghai 201203, China

Received 18 August 2013; Accepted 5 November 2013; Published 5 May 2014

Academic Editor: Shi-bing Su

Copyright © 2014 Jian-Jun Yan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This study was conducted to illustrate that auscultation features based on the fractal dimension combined with wavelet packet transform (WPT) were conducive to the identification the pattern of syndromes of Traditional Chinese Medicine (TCM). The WPT and the fractal dimension were employed to extract features of auscultation signals of 137 patients with lung Qi-deficient pattern, 49 patients with lung Yin-deficient pattern, and 43 healthy subjects. With these features, the classification model was constructed based on multiclass support vector machine (SVM). When all auscultation signals were trained by SVM to decide the patterns of TCM syndromes, the overall recognition rate of model was 79.49%; when male and female auscultation signals were trained, respectively, to decide the patterns, the overall recognition rate of model reached 86.05%. The results showed that the methods proposed in this paper were effective to analyze auscultation signals, and the performance of model can be greatly improved when the distinction of gender was considered.