Wrist blood flow signal-based computerized pulse diagnosis using spatial and spectrum features

Dong-Yu Zhang, Wang-Meng Zuo, David Zhang, Hong-Zhi Zhang, Nai-Min Li

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DOI: 10.4236/jbise.2010.34050                

Abstract

ABSTRACT Current computerized pulse diagnosis is mainly based on pressure and photoelectric signal. Considering the richness and complication of pulse diagnosis information, it is valuable to explore the feasibility of novel types of signal and to develop appropriate feature representation for diagnosis. In this paper, we present a study on computerized pulse diagnosis based on blood flow velocity signal. First, the blood flow velocity signal is collected using Doppler ultrasound device and preprocessed. Then, by locating the fiducial points, we extract the spatial features of blood flow velocity signal, and further present a Hilbert-Huang transform-based method for spectrum feature extraction. Finally, support vector machine is applied for computerized pulse diagnosis. Experiment results show that the proposed method is effective and promising in distinguishing healthy people from patients with cho- lecystitis or nephritis.

Keywords

Pulse Diagnosis; Blood Flow Velocity; Hilbert-Huang Transform; Support Vector Machine

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Li, S.Z. (1985) Pulse diagnosis. Hoc Ku Huyuh and G Seifert, Sydney.
[2]	Bob, F. (1995) The secret of Chinese pulse diagnosis. Blue Poppy Press, Boulder.
[3]	Mahesh, V., Kandaswamy, A., Vimal, C. and Sathish, B. (2009) ECG arrhythmia classification based on logistic model tree. Journal of Biomedical Science and Engineering, 2, 405-411.
[4]	Dickhaus, H. and Heinrich, H. (1996) Classifying bio- signals with wavelet networks: A method for noninvasive diagnosis. IEEE Engineering in Medicine and Biology Magazine, 15, 103-111.
[5]	McGuirk, S.P., Ewert, D., Barron, D.J. and Coote, J.H. (2009) Electrocardiographic interference and conductance volume measurements. Journal of Biomedical Science and Engineering, 2, 491-498.
[6]	Fei, Z.F. (2003) Contemporary sphygmology in traditional Chinese medicine. People’s Medical Publishing House, Beijing.
[7]	Wang, H. and Cheng, Y. (2005) A quantitative system for pulse diagnosis in traditional Chinese medicine. Proceedings of the 27th Annual Conference on Engineering in Medicine and Biology Society, 6, 5676-5679.
[8]	Fu, S.E. and Lai, S.P. (1989) A system for pulse measurement and analysis of Chinese medicine. Proceedings of the 11th Annual International Conference on Engineering in Medicine and Biology Society, 5, 1695-1696.
[9]	Tyan, C.C., Liu, S.H., Chen, J.Y., Chen, J.J. and Liang, W.M. (2008) A novel noninvasive measurement technique for analyzing the pressure pulse waveform of the radial artery. IEEE Transactions on Biomedical Engineering, 55, 288-297.
[10]	Hertzman, A.B. (1938) The blood supply of various skin areas as estimated by the photoelectric plethysmograph. American Journal Physiology, 124, 328-340.
[11]	Chun, T.L. and Ling, Y.W. (1983) Spectrum analysis of human pulse. IEEE Transactions on Biomedical Engineering, 30, 348-352.
[12]	Lu, W.A., Lin Wang, Y.Y. and Wang, W.K. (1999) Pulse analysis of patients with severe liver problems: Studying pulse spectrums to determine the effects on other organs. IEEE Engineering in Medicine and Biology Magazine, 18, 73-75.
[13]	Zhu, L., Yan, J., Tang, Q. and Li, Q. (2006) Recent progress in computerization of TCM. Journal of Communication and Computer, 3, 78-81.
[14]	Chen, C.Y., Wang, W.K., Kao, T., Yu, B.C. and Chiang, B.C. (1993) Spectral analysis of radial pulse in patients with acute uncomplicated myocardial infarction. Japanese Heart Journal, 34, 131-143.
[15]	Yu, G.L., Lin Wang, Y. Y. and Wang, W.K. (1994) Resonance in the kidney system of rats. American Journal Physiology Heart and Circulatory Physiology, 267, 1544- 1548.
[16]	Sigel, B. (1998) A brief history of Doppler ultrasound in the diagnosis of peripheral vascular disease. Ultrasound in Medicine and Biology, 24, 169-176.
[17]	Zhang, D.Y., Zhang, L., Zhang, D. and Zheng, Y. (2008) Wavelet based analysis of Doppler ultrasonic wrist pulse signals. Proceedings of the IEEE International Conference on Biomedical Engineering and Informatics, 2, 539- 543.
[18]	Zhang, D.Y., Wang, K.X., Wu, X.Q., Huang, B. and Li, N.M. (2009) Hilbert-Huang transform based Doppler blood flow signals analysis. Proceedings of the IEEE International Conference on Biomedical Engineering and Informatics, 1-5.
[19]	Lee, Y.J., Lee, J. and Kim, J.Y. (2008) A study on characteristics of radial arteries through ultrasonic waves. Proceedings of the 30th Annual International Conference on Engineering in Medicine and Biology Society, 2008, 2453-2456.
[20]	Xu, L.S., Zhang, D. and Wang, K.Q. (2005) Wavelet-based cascaded adaptive filter for removing baseline drift in pulse waveforms. IEEE Transactions on Biomedical Engineering, 52, 1973-1975.
[21]	Huang, N.E., Shen, Z., Long, S.R., Tung, C.C. and Liu, H.H. (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society, 454, 903-995.
[22]	Burges, C.J.C. (1998) A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2, 121-167.
[23]	Canu, S., Grandvalet, Y., Guigue, V. and Rakotomamonjy, A. (2005) SVM and Kernel methods Matlab toolbox, perception systèmes et information, INSA de Rouen, Rouen, France. http://asi.insa-rouen.fr/enseignants/~arak otom/toolbox/index.html
[24]	Guo, Q.L., Wang, K.Q., Zhang, D.Y. and Li, N.M. (2008) A wavelet packet based pulse waveform analysis for cholecystitis and nephrotic syndrome diagnosis. Proceedings of the 2008 International Conference on Wavelet Analysis and Pattern Recognition, 2, 513-517.