Table of Contents Author Guidelines Submit a Manuscript
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.

Linked References

  1. S. G. Zhao, “A modem research overview on the auscultation diagnosis of TCM,” Chinese Journal of the Practical Chinese with Modern Medicine, vol. 21, no. 14, pp. 1218–1220, 2008. View at Google Scholar
  2. X. M. Mo, Y. S. Zhang, and J. L. Zhang, “Current situation and prospect of auscultation research in TCM,” Chinese Journal of Basic Medicine in Traditional Chinese Medicine, vol. 4, no. 1, pp. 54–56, 1998. View at Google Scholar
  3. X. M. Mo, “Preliminary study on making use of phonograph for the diagnosis of deficiency syndrome of the lung with cough,” Journal of Traditional Chinese Medicine Research, no. 3, pp. 43–46, 1987. View at Google Scholar
  4. H. J. Wang, J. J. Yan, and Y. Q. Wang, “Digital technology for objective auscultation in traditional Chinese medical diagnosis,” in Proceedings of the International Conference on Audio, Language and Image Processing (ICALIP '08), pp. 1100–1104, July 2008.
  5. J. J. Yan, Y. Q. Wang, H. J. Wang et al., “Nonlinear analysis in TCM acoustic diagnosis using delay vector variance,” in Proceedings of the 2nd International Conference on Bioinformatics and Biomedical Engineering (iCBBE '08), pp. 2099–2102, May 2006.
  6. C.-C. Chiu, H.-H. Chang, and C.-H. Yang, “Objective auscultation for traditional Chinese medical diagnosis using novel acoustic parameters,” Computer Methods and Programs in Biomedicine, vol. 62, no. 2, pp. 99–107, 2000. View at Publisher · View at Google Scholar · View at Scopus
  7. J. J. Yan, S. Y. Mao, Y. Q. Wang et al., “Analysis of sound signal of five internal organs based on wavelet packet,” in Proceedings of IEEE International Conference on Bioinformatics & Biomedicine (BIBMW '10), pp. 707–711, December 2010.
  8. J. J. Yan, S. Y. Mao, Y. Q. Wang et al., “Wavelet packet based analysis of sound of five internal organs in TCM,” in Proceedings of the 3rd International Conference on Biomedical Engineering and Informatics (BMEI '10), pp. 1084–1088, October 2010.
  9. J. J. Yan, Y. Q. Wang, R. Guo et al., “Nonlinear analysis of auscultation signals in TCM using the combination of wavelet packet transform and sample entropy,” Evidence-Based Complementary and Alternative Medicine, vol. 2012, Article ID 247012, 9 pages, 2012. View at Publisher · View at Google Scholar
  10. G. S. Hu, Modern Digital Signal Processing, Tshinghua University Press, Beijing, China, 2004.
  11. C. Thompson, A. Mulpur, and V. Mehta, “Transition to chaos in acoustically driven flows,” Journal of the Acoustical Society of America, vol. 90, no. 4, pp. 2097–2103, 1991. View at Publisher · View at Google Scholar · View at Scopus
  12. P. Maragos, “Fractal aspects of speech signals: dimension and interpolation,” in Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP '91), pp. 417–420, 1991.
  13. Amended in National Clinical Professional Conference of Chronic Bronchitis, “The chronic bronchitis clinical diagnosis and curative effect judgment standard,” Chinese Journal of Tuberculosis and Respiratory Disease, vol. 3, no. 1, p. 61, 1980. View at Google Scholar
  14. X. Y. Zheng, Guideline for Clinical Study on New Drugs of Traditional Chinese Medicine, China Medicine Science and Technology Press, Beijing, China, 2002.
  15. State Bureau of Technical and Quality Supervision, Clinic Terminology of Traditional Chinese Medical Diagnosis and Treatment—Syndromes, Standards Press of China, Beijing, China, 1997.
  16. Y. Q. Wang, Diagnostics of Traditional Chinese Medicine, Chinese Medicine Science and Technology Press, Beijing, China, 2004.
  17. W. F. Zhu, Diagnostics of Traditional Chinese Medicine, Shanghai Science and Technology Press, Shanghai, China, 2006.
  18. L. Z. Hou and D. M. Han, “Selection of the vowel sound in the throat sound detection,” Journal of Audiology and Speech Diseases, vol. 10, no. 1, pp. 16–18, 2002. View at Google Scholar
  19. B. C. Li and J. S. Luo, Wavelet Analysis and Its Applications, Electronics Engineering Press, Beijing, China, 2003.
  20. X. H. Tang and Q. L. Li, Time-Frequency Analysis and Wavelet Transform, Science Press, Beijing, China, 2008.
  21. S.-Y. Wang, G.-X. Zhu, and Y.-Y. Tang, “Feature extraction using best wavelet packet transform,” Acta Electronica Sinica, vol. 31, no. 7, pp. 1035–1038, 2003. View at Google Scholar · View at Scopus
  22. G. Antonini and A. Orlandi, “Wavelet packet-based EMI signal processing and source identification,” IEEE Transactions on Electromagnetic Compatibility, vol. 43, no. 2, pp. 140–148, 2001. View at Publisher · View at Google Scholar · View at Scopus
  23. L. Pesu, P. Helisto, E. Ademovic et al., “Classification of respiratory sounds based on wavelet packet decomposition and learning vector quantization,” Technology and Health Care, vol. 6, no. 1, pp. 65–74, 1998. View at Google Scholar · View at Scopus
  24. A. H. Tewfik, D. Sinha, and P. Jorgensen, “On the optimal choice of a wavelet for signal representation,” IEEE Transactions on Information Theory, vol. 38, no. 2, pp. 747–765, 1992. View at Publisher · View at Google Scholar · View at Scopus
  25. Y. Y. Sun, C. D. Jin, and H. B. Zhang, “Fractal theory and application of fractal dimension,” Forestry Science and Technology Information, vol. 37, no. 4, pp. 8–9, 2005. View at Google Scholar
  26. H. Q. Li and F. Q. Wang, Fractal Theory and Its Application in Molecule Analysis, Science Press, Beijing, China, 1993.
  27. Y. Y. Zi, Z. J. He, and Z. S. Zhang, “Wavelet fractal technology and its applications to nonstationary fault diagnosis,” Journal of Xi'an Jiaotong University, vol. 34, no. 9, pp. 83–87, 2000. View at Google Scholar · View at Scopus
  28. V. N. Vapnik, Statistical Learning Theory, John Wiley & Sons, New York, NY, USA, 1998.
  29. Z. Q. Bian and X. G. Zhang, Pattern Recognition, Tsinghua University Press, Beijing, China, 2nd edition, 2000.
  30. F.-M. Tang, Z.-D. Wang, and M.-Y. Chen, “On multiclass classification methods for support vector machines,” Control and Decision, vol. 20, no. 7, pp. 746–749, 2005. View at Google Scholar · View at Scopus
  31. J. J. Yan, Q. W. Shen, R. Guo, Y. Q. Wang, C. H. Zhou, and J. T. Ren, “Multi-class learning with specific features for pairwise classes,” in Proceedings of the 4th International Conference on BioMedical Engineering and Informatics, pp. 2054–2057, 2011.
  32. C. C. Chang and C. J. Lin, “LIBSVM: a library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3, article 27, 2011. View at Publisher · View at Google Scholar