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Compound Pressure Signal Acquisition

  • David Zhang
  • Wangmeng Zuo
  • Peng Wang
Chapter

Abstract

In traditional Chinese pulse diagnosis (TCPD), to analyze the health condition of a patient, a practitioner should put three fingers on the wrist of the patient to adaptively feel the fluctuations in the radial pulse at the styloid processes. Thus, for comprehensive pulse signal acquisition, we should efficiently and accurately capture pulse signals at different positions and under different pressures. However, most conventional pulse signal acquisition devices can only capture signal at one position and under a fixed pressure and thus only capture limited pulse diagnostic information. In this chapter, we present a solution to the problems of sensor positioning, sensor array design, pressure adjustment, and mechanical structure design, resulting in a compound system for multiple-channel pulse signal acquisition. Compared with the other systems, this system provides a systematic solution to sensor positioning, is effective in measuring the width of the pulse, and can capture multichannel pulse signals together with sub-signals under different hold-down pressures.

References

  1. 1.
    S. Walsh, and E. King, Pulse Diagnosis: A Clinical Guide, Sydney Australia: Elsevier, 2008.Google Scholar
  2. 2.
    V. D. Lad, Secrets of the Pulse, Albuquerque, New Mexico: The Ayurvedic Press, 1996.Google Scholar
  3. 3.
    E. Hsu, Pulse Diagnosis in Early Chinese Medicine, New York, American: Cambridge University Press, 2010.Google Scholar
  4. 4.
    R. Amber, and B. Brooke, Pulse Diagnosis Detailed Interpretations For Eastern & Western Holistic Treatments, Santa Fe, New Mexico: Aurora Press, 1993.Google Scholar
  5. 5.
    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, no. 3, pp. 321-328, Jun, 2011.CrossRefGoogle Scholar
  6. 6.
    Y. Chen, L. Zhang, and D. Zhang, “Wrist pulse signal diagnosis using modified Gaussian Models and Fuzzy C-Means classification,” Medical Engineering & Physics, vol. 31, no. 10, pp. 1283-1289, Dec, 2009.CrossRefGoogle Scholar
  7. 7.
    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,” IEEE Transactions on Information Technology in Biomedicine, vol. 16, no. 8, pp. 599-607, Jul, 2012.Google Scholar
  8. 8.
    L. Liu, W. Zuo, D. Zhang, N. Li, and H. Zhang, “Classification of wrist pulse blood flow signal using time warp edit distance,” Medical Biometrics, vol. 6165, no. 1, pp. 137-144, 2010.CrossRefGoogle Scholar
  9. 9.
    D. Zhang, L. Zhang, and Y. Zheng, “Wavelet based analysis of doppler ultrasonic wrist-pulse signals,” in Proceedings of IEEE International Conference on Biomedical Engineering and Informatics, Hainan, China, 2008, pp. 539-543.Google Scholar
  10. 10.
    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, no. 4, pp. 361-366, 2010.CrossRefGoogle Scholar
  11. 11.
    D. Zhang, W. Zuo, Y. Li, and N. Li, “Gaussian ERP kernel classifier for pulse waveforms classification,” in Proceedings of IEEE International Conference on Pattern Recognition, Istanbul, Turkey 2010, pp. 2736-2739.Google Scholar
  12. 12.
    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 Proceedings of IEEE International Conference on Wavelet Analysis and Pattern Recognition, Hong Kong, China, 2008, pp. 513-517.Google Scholar
  13. 13.
    S. Charbonnier, S. Galichet, G. Mauris, and J. P. Siche, “Statistical and fuzzy models of ambulatory systolic blood pressure for hypertension diagnosis,” IEEE Transactions on Instrumentation and Measurement, vol. 49, no. 5, pp. 998-1003, 2000.CrossRefGoogle Scholar
  14. 14.
    H.-T. Wu, C.-H. Lee, C.-K. Sun, J.-T. Hsu, R.-M. Huang, and C.-J. Tang, “Arterial Waveforms Measured at the Wrist as Indicators of Diabetic Endothelial Dysfunction in the Elderly,” IEEE Transactions on Instrumentation and Measurement, vol. 61, no. 1, pp. 162-169, 2012.CrossRefGoogle Scholar
  15. 15.
    P. Dupuis, and C. Eugene, “Combined detection of respiratory and cardiac rhythm disorders by high-resolution differential cuff pressure measurement,” IEEE Transactions on Instrumentation and Measurement, vol. 49, no. 3, pp. 498-502, 2000.CrossRefGoogle Scholar
  16. 16.
    J. U. Kim, Y. J. Jeon, Y.-M. Kim, H. J. Lee, and J. Y. Kim, “Novel fiagnostic model for the deficient and excess pulse qualities,” Evidence-Based Complementary and Alternative Medicine, vol. 2012, no. 563958, pp. 1-11, 2012.Google Scholar
  17. 17.
    P. Zhang, and H. Wang, “A framework for automatic time-domain characteristic Parameters extraction of human pulse signals,” EURASIP Journal on Advances in Signal Processing, vol. 2008, no. 468390, pp. 1-9, 2008.CrossRefGoogle Scholar
  18. 18.
    C. Chen, E. Nevo, B. Fetics, P. H. Pak, F. C. P. Yin, L. Maughan, and D. A. Kass, “Estimation of central aortic pressure waveform by mathematical transformation of radial tonometry pressure: validation of generalized transfer function,” Circulation, vol. 95, no. 7, pp. 1827-1836, 1997.CrossRefGoogle Scholar
  19. 19.
    S. Lu, R. Wang, L. Cui, Z. Zhao, Y. Yu, and Z. Shan, “Wireless networked Chinese telemedicine system: method and apparatus for remote pulse information retrieval and diagnosis,” in Proceedings of IEEE International Conference on Pervasive Computing and Communications, Hong Kong, China, 2008, pp. 698-703.Google Scholar
  20. 20.
    C. C. Tyan, S. H. Liu, J. Y. Chen, J. J. Chen, and W. M. Liang, “A novel noninvasive measurement technique for analyzing the pressure pulse waveform of the radial artery,” IEEE Transactions on Biomedical Engineering, vol. 55, no. 1, pp. 288-297, Jan, 2008.CrossRefGoogle Scholar
  21. 21.
    C.-S. Hu, Y.-F. Chung, C.-C. Yeh, and C.-H. Luo, “Temporal and Spatial Properties of Arterial Pulsation Measurement Using Pressure Sensor Array,” Evidence-Based Complementary and Alternative Medicine, vol. 2012, pp. 1-9, 2012.Google Scholar
  22. 22.
    H. Sorvoja, V. M. Kokko, R. Myllyla, and J. Miettinen, “Use of EMFi as a blood pressure pulse transducer,” IEEE Transactions on Instrumentation and Measurement, vol. 54, no. 6, pp. 2505-2512, 2005.CrossRefGoogle Scholar
  23. 23.
    E. Kaniusas, H. Pfutzner, L. Mehnen, J. Kosel, C. Tellez-Blanco, G. Varoneckas, A. Alonderis, T. Meydan, M. Vazquez, M. Rohn, A. M. Merlo, and B. Marquardt, “Method for continuous nondisturbing monitoring of blood pressure by magnetoelastic skin curvature sensor and ECG,” IEEE Sensors Journal, vol. 6, no. 3, pp. 819-828, Jun, 2006.CrossRefGoogle Scholar
  24. 24.
    L. Chen, H. Atsumi, M. Yagihashi, F. Mizuno, H. Narita, and H. Fujimoto, “A preliminary research on analysis of pulse diagnosis,” in Proceedings of IEEE International Conference on Complex Medical Engineering, Beijing, China, 2007, pp. 1807-1812.Google Scholar
  25. 25.
    H.-T. Wu, C.-H. Lee, and A.-B. Liu, “Assessment of endothelial function using arterial pressure signals,” Journal of Signal Processing Systems, vol. 64, no. 2, pp. 223-232, 2011.CrossRefGoogle Scholar
  26. 26.
    ISO, IEC, OIML, and BIPM, Guide to the Expression of Uncertainty in Measurement, Geneva: ISO, 1995.Google Scholar
  27. 27.
    B. Dobkin, and J. Williams, Analog circuit design: a tutorial guide to applications and solutions, America: Newnes, 2011.Google Scholar
  28. 28.
    D. A. Fedosov, W. Pan, B. Caswell, G. Gompper, and G. E. Karniadakis, “Predicting human blood viscosity in silico,” Proceedings of the National Academy of Sciences, vol. 108, no. 29, pp. 11772-11777, 2011.CrossRefGoogle Scholar
  29. 29.
    I. Wakabayashi, and H. Masuda, “Association of pulse pressure with fibrinolysis in patients with type 2 diabetes,” Thrombosis Research, vol. 121, no. 1, pp. 95-102, 2007.CrossRefGoogle Scholar
  30. 30.
    N. Arunkumar, and K. M. M. Sirajudeen, “Approximate entropy based ayurvedic pulse diagnosis for diabetics - a case study,” in Proceedings of IEEE International Conference on Trendz in Information Sciences and Computing, Chennai, India, 2011, pp. 133-135.Google Scholar
  31. 31.
    L. Xu, D. Zhang, and K. Wang, “Wavelet-based cascaded adaptive filter for removing baseline drift in pulse waveforms,” IEEE Transactions on Biomedical Engineering, vol. 52, no. 11, pp. 1973-1975, Nov, 2005.CrossRefGoogle Scholar
  32. 32.
    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 Proceedings of Sino-foreign-interchange Workshop on Intelligence Science and Intelligent Data Engineering, NanJing China, 2012.Google Scholar
  33. 33.
    D. E. Lake, J. S. Richman, M. P. Griffin, and J. R. Moorman, “Sample entropy analysis of neonatal heart rate variability,” American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, vol. 283, no. 3, pp. R789-R797, Sep, 2002.CrossRefGoogle Scholar
  34. 34.
    J. S. Richman, and J. R. Moorman, “Physiological time-series analysis using approximate entropy and sample entropy,” American Journal of Physiology-Heart and Circulatory Physiology, vol. 278, no. 6, pp. H2039-H2049, Jun, 2000.CrossRefGoogle Scholar
  35. 35.
    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, no. 3, pp. 331-339, Jun, 2010.CrossRefGoogle Scholar
  36. 36.
    S. M. Pincus, “Approximate entropy as a measure of system-complexity,” Proceedings of the National Academy of Sciences, vol. 88, no. 6, pp. 2297-2301, Mar, 1991.MathSciNetCrossRefGoogle Scholar
  37. 37.
    M. Costa, A. L. Goldberger, and C. K. Peng, “Multiscale entropy analysis of complex physiologic time series,” Physical Review Letters, vol. 89, no. 068102, pp. 1-4, Aug 5, 2002.Google Scholar
  38. 38.
    M. Costa, A. Goldberger, and C. K. Peng, “Multiscale entropy to distinguish physiologic and synthetic RR time series,” in Proceedings of Computers in Cardiology, Memphis, America, 2002, pp. 137-140.Google Scholar
  39. 39.
    M. Costa, A. L. Goldberger, and C. K. Peng, “Multiscale entropy analysis of biological signals,” Physical Review E, vol. 71, no. 021906, pp. 1-18, Feb, 2005.MathSciNetGoogle Scholar
  40. 40.
    C. J. C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 121-167, Jun, 1998.CrossRefGoogle Scholar
  41. 41.
    A. G. Lalkhen, and A. McCluskey, “Clinical tests: sensitivity and specificity,” Continuing Education in Anaesthesia, Critical Care & Pain, vol. 8, no. 6, pp. 221-223, 2008.CrossRefGoogle Scholar
  42. 42.
    J. Platt, “Probabilistic Outputs for Support Vector Machines and Comparison to Regularized Likelihood Methods,” Proceedings of Advances in Large Margin Classifiers, pp. 61-74, 2000.Google Scholar
  43. 43.
    D. Jia, N. Li, S. Liu, and S. Li, “Decision level fusion for pulse signal classification using multiple features,” in Proceedings of IEEE International Conference on Biomedical Engineering and Informatics, Yantai, China, 2010, pp. 843-847.Google Scholar
  44. 44.
    J. Kittler, M. Hatef, P. W. Duin, and J. Matas, “On Combining Classifiers,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, pp. 226-239, 1998.CrossRefGoogle Scholar
  45. 45.
    Q. McNemar, “Note on the sampling error of the difference between correlated proportions or percentages,” Psychometrika, vol. 12, pp. 153-157, 1947.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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