Modified Gaussian Models and Fuzzy C-Means

  • David Zhang
  • Wangmeng Zuo
  • Peng Wang


In this chapter, a systematic approach is proposed to analyze the computation wrist pulse signals, with the focus placed on the feature extraction and pattern classification. The wrist pulse signals are first collected and preprocessed. Considering that a typical pulse signal is composed of periodically systolic and diastolic waves, a modified Gaussian model is adopted to fit the pulse signal and the modeling parameters are then taken as features. Consequently, a feature selection scheme is proposed to eliminate the tightly correlated features and select the disease-sensitive ones. Finally, the selected features are fed to a Fuzzy C-Means (FCM) classifier for pattern classification. The proposed approach is tested on a dataset which includes pulse signals from 100 healthy persons and 88 patients. The results demonstrate the effectiveness of the proposed approach in computation wrist pulse diagnosis.


  1. 1.
    Lukman S, He Y, Hui S. “Computational methods for traditional Chinese medicine: a survey,” Computer Methods and Programs in Biomedicine 2007;88:283–94.CrossRefGoogle Scholar
  2. 2.
    Hammer L. Chinese pulse diagnosis—contemporary approach. Eastland Press; 2001.Google Scholar
  3. 3.
    Zhu L, Yan J, Tang Q, Li Q. “Recent progress in computerization of TCM,” Journal of Communication and Computer 2006;3(7).Google Scholar
  4. 4.
    Wang K, Xu L, Zhang D, Shi C. “TCPD based pulse monitoring and analyzing,” In: Proceedings of the 1st ICMLC conference. 2002.Google Scholar
  5. 5.
    Wang H, Cheng Y. “A quantitative system for pulse diagnosis in Traditional Chinese Medicine,” In: Proceedings of the 27th IEEE EMB conference. 2005.Google Scholar
  6. 6.
    Lau E, Chwang A. “Relationship between wrist-pulse characteristics and body conditions,” In: Proceedings of the EM2000 conference. 2000.Google Scholar
  7. 7.
    Lu W, Wang Y, Wang W. “Pulse analysis of patients with severe liver problems,” IEEE Engineering in Medicine and Biology Magazine 1999;18(January/February (1)):73–5.Google Scholar
  8. 8.
    Wang B, Luo J, Xiang J, Yang Y. “Power spectral analysis of human pulse and study of traditional Chinese medicine pulse-diagnosis mechanism,” Journal of Northwestern University (Natural Science Edition) 2001;31(1):22–5.Google Scholar
  9. 9.
    Wang Y, Wu X, Liu B, Yi Y. “Definition and application of indices in Doppler ultrasound sonogram,” Journal of Biomedical Engineering of Shanghai 1997;18:26–9.Google Scholar
  10. 10.
    Ruano M, Fish P. “Cost/benefit criterion for selection of pulsed Doppler ultrasound spectral mean frequency and bandwidth estimators,” IEEE Transactions on BME 1993;40:1338–41.CrossRefGoogle Scholar
  11. 11.
    Leonard P, Beattie TF, Addison PS, Watson JN. “Wavelet analysis of pulse oximeter waveform permits identification of unwell children,” Journal of Emergency Medicine 2004;21:59–60.CrossRefGoogle Scholar
  12. 12.
    Zhang Y, Wang Y, Wang W, Yu J. “Wavelet feature extraction and classification of Doppler ultrasound blood flow signals,” Journal of Biomedical Engineering 2002;19(2):244–6.Google Scholar
  13. 13.
    Zhang D, Zhang L, Zhang D, Zheng Y. “Wavelet based analysis of Doppler ultrasonic wrist-pulse signals,” In: Proceedings of the ICBBE 2008 conference, vol. 2. 2008. p. 539–43.Google Scholar
  14. 14.
    Chen B, Wang X, Yang S, McGreavy C. “Application of wavelets and neural networks to diagnostic system development, 1, feature extraction,” Computers and Chemical Engineering 1999;23:899–906.CrossRefGoogle Scholar
  15. 15.
    Heral A, Hou Z. “Application of wavelet approach for ASCE structural health monitoring benchmark studies,” Journal of Engineering Mechanics 2004;1:96–104.CrossRefGoogle Scholar
  16. 16.
    Burges C. “A tutorial on support vector machines for pattern recognition,” Data Mining and Knowledge Discovery 1998;2:121–67.CrossRefGoogle Scholar
  17. 17.
    Chiu C, Yeh S, Yu Y. “Classification of the pulse signals based on self-organizing neural network for the analysis of the autonomic nervous system,” Chinese Journal of Medical and Biological Engineering 1996;16: 461–76.Google Scholar
  18. 18.
    Chen Y, Zhang L, Zhang D, Zhang D. “Pattern classification for Doppler ultrasonic wrist pulse signals,” In: 5th ICBBE conference. 2009.CrossRefGoogle Scholar
  19. 19.
    Yoon Y, Lee M, Soh K. “Pulse type classification by varying contact pressure,” IEEE Engineering in Medicine and Biology Magazine 2000;19:106–10.Google Scholar
  20. 20.
    Xu L, Zhang D, Wang K. “Wavelet-based cascaded adaptive filter for removing baseline drift in pulse waveforms,” IEEE Transactions on Biomedical Engineering 2005;52(11):1973–5.CrossRefGoogle Scholar
  21. 21.
    Xia C, Li Y, Yan J, Wang Y, Yan H, Guo R, et al. “A practical approach to wrist pulse segmentation and single-period average waveform estimation,” In: The ICBEI conference. 2008. p. 334–8.CrossRefGoogle Scholar
  22. 22.
    Walsh S, King E. Pulse diagnosis: a clinical guide. Elsevier; 2007.Google Scholar
  23. 23.
    Shu J, Sun Y. “Developing classification indices for Chinese pulse diagnosis,” Complementary Therapies in Medicine 2007;15:190–8.CrossRefGoogle Scholar
  24. 24.
    More JJ. “Recent developments in algorithms and software for trust region methods,” In: Mathematical programming. NY: Springer-Verlag; 1983. p. 258–87.Google Scholar
  25. 25.
    Mor JJ. The Levenberg–Marquardt algorithm: implementation and theory. Berlin/Heidelberg: Springer; 2006.Google Scholar
  26. 26.
    Jorge N, Stephen W. Numerical optimization. New York: Springer; 1999.zbMATHGoogle Scholar
  27. 27.
    Bezdek JC. Pattern recognition with fuzzy objective function algorithms, New York: Plenum Press; 1981.CrossRefGoogle Scholar
  28. 28.
    Wang X, Wang Y, Wang L. “Improving fuzzy c-means clustering based on feature weight learning,” Pattern Recognition Letters 2004;25:1123–32.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • David Zhang
    • 1
  • Wangmeng Zuo
    • 2
  • Peng Wang
    • 3
  1. 1.School of Science and EngineeringThe Chinese University of Hong KongShenzhenChina
  2. 2.Harbin Institute of TechnologyHarbinChina
  3. 3.Northeast Agricultural UniversityHarbinChina

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