Coronary Heart Disease Recognition Based on Dynamic Pulse Rate Variability

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• 关键词：Pulse signal ; Dynamic pulse rate variability (DPRV) ; Coronary heart disease recognition
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9771
• 期：1
• 页码：28-38
• 全文大小：331 KB
• 参考文献：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)
  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)CrossRef
  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)CrossRef
  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)CrossRef
  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)
  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)CrossRef
  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)CrossRef
  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)
  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)
  10.The Physionet/Fantasia database. http://​www.​physionet.​org/​physiobank/​database/​fantasia
  11.The Physionet/The MGH/MF waveform database. http://​www.​physionet.​org/​physiobank/​database/​mghdb/​
  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)
  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)
  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)
  
• 作者单位：Aihua Zhang (16)
  Boxuan Wei (16)
  Yongxin Chou (17)
  
  16. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050, China
  17. School of Electrical and Automatic Enginnering, Changshu Institute of Technology, Changshu, 215500, China
  
• 丛书名：Intelligent Computing Theories and Application
• ISBN：978-3-319-42291-6
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9771

文摘

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.