Abstract
Humans have a variety of pulse waveforms based on their physiological state. The recognition of the feature points of the pulse wave is very helpful in analyzing the body’s physiological and pathological conditions and in preventing and diagnosing cardiovascular diseases. This paper proposes an accurate recognition algorithm of the feature points based on wavelet analysis and time domain characteristics of the pulse wave. Further, this study examines the Hidden Markov Model and performs non-invasive blood pressure estimation on a number of subjects. The experiments show that the algorithm can effectively identify the feature points of the pulse wave. The results of the model are consistent with the actual measurement results and the correlation coefficient reached 96 %, which indicates that the algorithm is significant as a method for non-invasive blood pressure monitoring.
Similar content being viewed by others
References
Luo, Z. C., Zhang, S., Yang, W. M., et al. (1996). Study pulse waveform feature information. Journal of Beijing University of Technology, 1, 71–79.
Saito M., Matsukawa M., Asada T., et al. (2012). Noninvasive assessment of arterial stiffness by pulse wave analysis. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 59(11).
Geddes, L. A., Voelz, M., Combs, C., et al. (1982). Characterization of the oscillometric method for measuring indirect blood pressure. Annals of Biomedical Engineering, 10(6), 271–280.
Li, Z., Zhao, X., Cheng, X. O., et al. (1989). Automatic identification pulse wave feature point. Information and Control, 18(1), 59–64.
Zhang X., Xu L., Chen K., et al. (2009). A new method for locating feature points in pulse wave using wavelet transform. Computer Science and Information Engineering, 2009 WRI World Congress on. IEEE 5,367-371.
Sun, W., Tang, N., Jiang, G. P. (2015). Study of characteristic point identification and preprocessing method for pulse wave signals. Journal of Biomedical Engineering, (1).
Hong, Y. X., Xing, W., & Li, F. (2005). Application of syntactic pattern recognition in research on pulse wave’s characteristic information. Chinese Journal of Medical Instrumentation, 29(5), 325–327.
Payne, R. A., Symeonides, C. N., Webb, D. J., et al. (2006). Pulse transit time measured from the ECG: an unreliable marker of beat-to-beat blood pressure. Journal of Applied Physiology, 100(1), 136–141.
Zakaria, N. A., Sharifmuddin, N. B., Ridzwan, W. M. F. W. M., et al. (2010). Pulse wave transit time and its relationship with systolic blood pressure. 6th World Congress of Biomechanics (WCB 2010). August 1–6, 2010 Singapore. Springer Berlin Heidelberg, pp 1354–1357.
Jiao, X. J., & Fang, X. Y. (2002). Research on continuous measurement of blood pressure via characteristic parameters of pulse wave. Journal of Biomedical Engineering, 19(2), 217–220.
Zahid, D., Mceniery, C. M., Cockcroft, J. R., et al. (2006). Atenolol and eprosartan: differential effects on central blood pressure and aortic pulse wave velocity. American Journal of Hypertension, 19(2), 214–219.
O’Rourke, M. F., & Gallagher, D. E. (1996). Pulse wave analysis. Journal of Hypertension Supplement, 14, S147–S158.
Zhang, P. Y., & Wang, H. Y. (2008). A framework for automatic time-domain characteristic parameters extraction of human pulse signals. EURASIP Journal on Advances in Signal Processing, 2008, 55.
Yuan H. L., Qi, H. Y., Hong, F. S. (2009). Pulse feature analysis and extraction based on pulse mechanism analysis. Computer Science and Information Engineering, 2009 WRI World Congress on. IEEE, 7:53–56.
Rangaprakash, D., & Dutt, D. N. (2015). Study of wrist pulse signals using time domain spatial features. Computers and Electrical Engineering, 45(C), 100–107.
Baum, L. E., & Eagon, J. A. (1967). An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology. Bulletin of the American Mathematical Society, 73(3), 360–363.
Baum, L. E., Petrie, T., Soules, G., et al. (1970). A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. The Annals of Mathematical Statistics, 164–171.
Rabiner, L. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2), 257–286.
Ali Hassan, M. K., Mashor, M. Y., Mohd Saad, A. R., et al. (2011). Non-invasive continuous blood pressure monitoring based on PWTT. Journal of Advanced Computer Science & Technology Research, 12(11), 1616–1627.
Ling, Z. (2013). Design of sleeveless blood pressure measuring instrument based on pulse wave transit time. Journal of Electronic Measurement & Instrument, 26(12), 10801085.
Acknowledgments
The present work is supported by the National Science Foundation of China (81371713) and the Fundamental Research Funds for the Central Universities (106112015CDJZR235522). The authors would like to thank for critically reviewing the manuscript.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Liu, X., Ji, Z. & Tang, Y. Recognition of Pulse Wave Feature Points and Non-invasive Blood Pressure Measurement. J Sign Process Syst 87, 241–248 (2017). https://doi.org/10.1007/s11265-016-1178-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11265-016-1178-6