Coronary Heart Disease Recognition Based on Dynamic Pulse Rate Variability

  • Aihua ZhangEmail author
  • Boxuan Wei
  • Yongxin Chou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9771)


Objective: In order to improve the accuracy and real-time of coronary heart disease (CHD) recognition, we propose a new method to analyze the pulse signal with the idea of sliding window iterative. Methods: Firstly, the principle of the feature extraction method(including time domain method, Poincare plot and information entropy) that combined with the idea of sliding window iterative is described. Secondly, The continuous blood pressure signals from the website database PhysioNet are chosen to generate the dynamic pulse rate variability (DPRV) signal as experimental data, and the linear and nonlinear feature is selected for classifying the healthy people and patients with CHD. Finally, the running time and accuracy of the method in this paper are comparaed with other methods. Result: The pulse signal can be online analyzed by this method. The average recognizing accuracy is 97.6 %. Conclusion: This methods is entirely feasible. Compared with existing methods, its accuracy and real-time is higher.


Pulse signal Dynamic pulse rate variability (DPRV) Coronary heart disease recognition 



This work was supported by the National Natural Science Foundation (grant 81360229) of China, the National Key Laboratory Open Project Foundation (grant 201407347) of Pattern Recognition in China and the Gansu Province Basic Research Innovation Group Project (1506RJIA031).


  1. 1.
    Yu, E., He, D., Su, Y., et al.: Feasibility analysis for pulse rate variability to replace heart rate variability of the healthy subjects. In: 2013 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 1065–1070 (2013)Google Scholar
  2. 2.
    Kim, W.-S., Jin, S.-H., Park, Y.K., Choi, H.-M.: A study on development of multi-parametric measure of heart rate variability diagnosing cardiovascular disease. In: Magjarevic, R., Nagel, J.H. (eds.) World Congress on Medical Physics and Biomedical Engineering 2006. IFMBE Proceedings, vol. 14, pp. 3480–3483. Springer, Berlin (2007)CrossRefGoogle Scholar
  3. 3.
    Lee, H.G., Noh, K.Y., Ryu, K.H.: Mining biosignal data: coronary artery disease diagnosis using linear and nonlinear features of HRV. In: Washio, T., et al. (eds.) PAKDD 2007. LNCS (LNAI), vol. 4819, pp. 218–228. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Dua, S., Du, X., Sree, S.V., et al.: Novel classification of coronary artery disease using heart rate variability analysis. J. Mech. Med. Biol. 12(4), 1240017 (2012)CrossRefGoogle Scholar
  5. 5.
    Karimi, M., Amirfattahi, R., Sadri, S., et al.: Noninvasive detection and classification of coronary artery occlusions using wavelet analysis of heart sounds with neural networks. In: 3rd IEE International Seminar on Medical Applications of Signal Processing, pp. 117–120 (2005)Google Scholar
  6. 6.
    Babaoglu, İ., Findik, O., Ülker, E.: A comparison of feature selection models utilizing binary particle swarm optimization and genetic algorithm in determining coronary artery disease using support vector machine. Expert Syst. Appl. 37(4), 3177–3183 (2010)CrossRefGoogle Scholar
  7. 7.
    Babaoğlu, I., Fındık, O., Bayrak, M.: Effects of principle component analysis on assessment of coronary artery diseases using support vector machine. Int. J. Expert Syst. Appl. 37(3), 2182–2185 (2010)CrossRefGoogle Scholar
  8. 8.
    Yu, E., He, D., Su, Y., et al.: Feasibility analysis for pulse rate variability to replace heart rate variability of the healthy subjects. In: 2013 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 1065–1070 (2013)Google Scholar
  9. 9.
    Yongxin, C., Zhang, A., Jiqing, O.U., et al.: Dynamic pulse signal processing and analyzing in mobile system. Chin. J. Med. Instrum. 05, 313–317 (2015)Google Scholar
  10. 10.
  11. 11.
    The Physionet/The MGH/MF waveform database.
  12. 12.
    Chou, Y., Zhang, A., Yang, X.: Dynamic pulse rate variability extraction method based on improved sliding window iterative DFT. Chin. J. Sci. Instrum. 36(4), 812–821 (2015)Google Scholar
  13. 13.
    Bian, C.H., Ma, Q.L., Si, J.F., et al.: Entropy analysis method of short time heart rate variability symbol sequence. Chin. Sci. Bull. 03, 340–344 (2009)Google Scholar
  14. 14.
    Wang, J.S., Chiang, W.C., Hsu, Y.L., Yang, Y.T.: ECG arrhythmia classification using a probabilistic neural network with a feature reduction method. Neuro Comput. 116, 38–45 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.College of Electrical and Information EngineeringLanzhou University of TechnologyLanzhouChina
  2. 2.School of Electrical and Automatic EnginneringChangshu Institute of TechnologyChangshuChina

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