Advertisement

Generalized Feature Extraction for Wrist Pulse Analysis: From 1-D Time Series to 2-D Matrix

  • David Zhang
  • Wangmeng Zuo
  • Peng Wang
Chapter

Abstract

Though many literatures on pulse feature extraction have been published, they just handle the pulse signals as simple 1-D time series and ignore the information within the class. This chapter presents a generalized method of pulse feature extraction, extending the feature dimension from 1-D time series to 2-D matrix. The conventional wrist pulse features correspond to a particular case of the generalized models. The proposed method is validated through pattern classification on actual pulse records. Both quantitative and qualitative results relative to the 1-D pulse features are given through diabetes diagnosis. The experimental results show that the generalized 2-D matrix feature is effective in extracting both the periodic and nonperiodic information. And it is practical for wrist pulse analysis.

References

  1. 1.
    G. Maciocia, S. Foster, W. H. Hylton, R. Weiss, M. Tierra, H. Santillo, et al., “The foundations of Chinese medicine: A comprehensive text,” London, UK: Churchill Livingstone, 2005.Google Scholar
  2. 2.
    G. Maciocia, Diagnosis in Chinese medicine: a comprehensive guide: Elsevier Health Sciences, 2013.Google Scholar
  3. 3.
    S. Lukman, Y. He, and S.-C. Hui, “Computational methods for Traditional Chinese Medicine: A survey,” Computer Methods and Programs in Biomedicine, vol. 88, pp. 283–294, 12.2007.CrossRefGoogle Scholar
  4. 4.
    J.-J. Shu and Y. Sun, “Developing classification indices for Chinese pulse diagnosis,” Complementary Therapies in Medicine, vol. 15, pp. 190–198, 9, 2007.CrossRefGoogle Scholar
  5. 5.
    S. S. Franklin, S. A. Khan, N. D. Wong, M. G. Larson, and D. Levy, “Is pulse pressure useful in predicting risk for coronary heart disease? The Framingham Heart Study,” Circulation, vol. 100, pp. 354–360, 1999.CrossRefGoogle Scholar
  6. 6.
    Y. Chen, L. Zhang, D. Zhang, and D. Zhang, “Wrist pulse signal diagnosis using modified Gaussian models and Fuzzy C-Means classification,” Medical engineering & physics, vol. 31, pp. 1283–1289, 2009.CrossRefGoogle Scholar
  7. 7.
    S. A. CARTER, “Indirect systolic pressures and pulse waves in arterial occlusive disease of the lower extremities,” Circulation, vol. 37, pp. 624–637, 1968.CrossRefGoogle Scholar
  8. 8.
    H. M. Haqqani, J. B. Morton, and J. M. Kalman, “Using the 12-Lead ECG to Localize the Origin of Atrial and Ventricular Tachycardias: Part 2—Ventricular Tachycardia,” Journal of cardiovascular electrophysiology, vol. 20, pp. 825–832, 2009.CrossRefGoogle Scholar
  9. 9.
    D. Wang, D. Zhang, and G. Lu, “A Novel Multichannel Wrist Pulse System With Different Sensor Arrays,” Instrumentation and Measurement, IEEE Transactions on, vol. PP, pp. 1–1, 2015. doi:  https://doi.org/10.1109/TIM.2014.2357599 CrossRefGoogle Scholar
  10. 10.
    D. Wang and D. Zhang, “Analysis of pulse waveforms preprocessing,” in Computerized Healthcare (ICCH), 2012 International Conference on, 2012, pp. 175–180.Google Scholar
  11. 11.
    I. S. N. Murthy and G. Sita, “Digital models for arterial pressure and respiratory waveforms,” Biomedical Engineering, IEEE Transactions on, vol. 40, pp. 717–726, 1993.Google Scholar
  12. 12.
    L. Xu, M. Q.-H. Meng, K. Wang, W. Lu, and N. Li, “Pulse images recognition using fuzzy neural network,” Expert systems with applications, vol. 36, pp. 3805–3811, 2009.CrossRefGoogle Scholar
  13. 13.
    L. Liu, N. Li, W. Zuo, D. Zhang, and H. Zhang, “Multiscale sample entropy analysis of wrist pulse blood flow signal for disease diagnosis,” in Intelligent Science and Intelligent Data Engineering, Springer, 2013, pp. 475–482.Google Scholar
  14. 14.
    Y. Chen, D. Zhang, D. Zhang, and Z. Dongyu, “Pattern Classification for Doppler Ultrasonic Wrist Pulse Signals,” in Bioinformatics and Biomedical Engineering , 2009. ICBBE 2009. 3rd International Conference on, 2009, pp. 1–4.Google Scholar
  15. 15.
    Y. Chen, L. Zhang, D. Zhang, and D. Zhang, “Computerized wrist pulse signal diagnosis using modified auto-regressive models,” Journal of Medical Systems, vol. 35, pp. 321–328, 2011.CrossRefGoogle Scholar
  16. 16.
    D. Wang, D. Zhang, and G. Lu. “A robust signal preprocessing framework for wrist pulse analysis.” Biomedical Signal Processing and Control, vol. 23, pp. 62–75, 2016. CrossRefGoogle Scholar
  17. 17.
    D.-Y. Zhang, W.-M. Zuo, D. Zhang, H.-Z. Zhang, and N.-M. Li, “Wrist blood flow signal-based computerized pulse diagnosis using spatial and spectrum features,” Journal of Biomedical Science and Engineering, vol. 3, p. 361, 2010.CrossRefGoogle Scholar
  18. 18.
    Q.-L. Guo, K.-Q. Wang, D.-Y. Zhang, and N.-M. Li, “A wavelet packet based pulse waveform analysis for cholecystitis and nephrotic syndrome diagnosis,” in Wavelet Analysis and Pattern Recognition, 2008. ICWAPR’08. International Conference on, 2008, pp. 513–517.Google Scholar
  19. 19.
    L. Liu, W. Zuo, D. Zhang, N. Li, and H. Zhang, “Combination of heterogeneous features for wrist pulse blood flow signal diagnosis via multiple kernel learning,” Information Technology in Biomedicine, IEEE Transactions on, vol. 16, pp. 598–606, 2012.Google Scholar
  20. 20.
    L. Xu, M. Q.-H. Meng, C. Shi, K. Wang, and N. Li, “Quantitative analyses of pulse images in Traditional Chinese Medicine,” Medical Acupuncture, vol. 20, pp. 175–189, 2008.CrossRefGoogle Scholar
  21. 21.
    Y.-F. Chung, C.-S. Hu, C.-C. Yeh, and C.-H. Luo, “How to standardize the pulse-taking method of traditional Chinese medicine pulse diagnosis,” Computers in Biology and Medicine, vol. 43, pp. 342–349, 5/1/ 2013.CrossRefGoogle Scholar
  22. 22.
    L. Xu, M. Q.-H. Meng, X. Qi, and K. Wang, “Morphology variability analysis of wrist pulse waveform for assessment of arteriosclerosis status,” Journal of medical systems, vol. 34, pp. 331–339, 2010.CrossRefGoogle Scholar
  23. 23.
    S.-D. Bao, Y.-T. Zhang, and L.-F. Shen, “Physiological signal based entity authentication for body area sensor networks and mobile healthcare systems,” in Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the, 2005, pp. 2455–2458.Google 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

Personalised recommendations