KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Classification of Premature Ventricular Contraction using Error Back-Propagation

  • Jeon, Eunkwang (Department of Computer Science & Engineering, Soonchunhyang University) ;
  • Jung, Bong-Keun (Department of Occupational Therapy, Soonchunhyang University) ;
  • Nam, Yunyoung (Department of Computer Engineering, Soonchunhyang University) ;
  • Lee, HwaMin (Department of Computer Software Engineering, Soonchunhyang University)
  • Received : 2017.10.19
  • Accepted : 2017.12.28
  • Published : 2018.02.28
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Abstract

Arrhythmia has recently emerged as one of the major causes of death in Koreans. Premature Ventricular Contraction (PVC) is the most common arrhythmia that can be found in clinical practice, and it may be a precursor to dangerous arrhythmias, such as paroxysmal insomnia, ventricular fibrillation, and coronary artery disease. Therefore, we need for a method that can detect an abnormal heart beat and diagnose arrhythmia early. We extracted the features corresponding to the QRS pattern from the subject's ECG signal and classify the premature ventricular contraction waveform using the features. We modified the weighting and bias values based on the error back-propagation algorithm through learning data. We classify the normal signal and the premature ventricular contraction signal through the modified weights and deflection values. MIT-BIH arrhythmia data sets were used for performance tests. We used RR interval, QS interval, QR amplitude and RS amplitude features. And the hidden layer with two nodes is composed of two layers to form a total three layers (input layer 0, output layer 3).

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References

  1. Schijvenaars, Bob JA, Gerard van Herpen, and Jan A. Kors, "Intraindividual variability in electrocardiograms," Journal of Electrocardiology, vol. 41, no. 3, 190-196, 2008. https://doi.org/10.1016/j.jelectrocard.2008.01.012
  2. Chan, Adrian DC, et al. "Wavelet distance measure for person identification using electrocardiograms," IEEE transactions on instrumentation and measurement, vol. 57, no. 2, 248-253, 2008. https://doi.org/10.1109/TIM.2007.909996
  3. Chauhan, Sushil, A. S. Arora, and Amit Kaul, "A survey of emerging biometric modalities," Procedia Computer Science, vol. 2, 213-218, 2010. https://doi.org/10.1016/j.procs.2010.11.027
  4. Dutta, Saibal, Amitava Chatterjee, and Sugata Munshi, "Correlation technique and least square support vector machine combine for frequency domain based ECG beat classification," Medical engineering & physics, vol. 32, no. 10, 1161-1169, 2010. https://doi.org/10.1016/j.medengphy.2010.08.007
  5. Andreao, Rodrigo Varejao, Bernadette Dorizzi, and Jerome Boudy, "ECG signal analysis through hidden Markov models," IEEE Transactions on Biomedical engineering, vol. 53, no. 8, 1541-1549, 2006. https://doi.org/10.1109/TBME.2006.877103
  6. Kadish, Alan H., et al, "ACC/AHA clinical competence statement on electrocardiography and ambulatory electrocardiography: A report of the ACC/AHA/ACP-ASIM Task Force on Clinical Competence (ACC/AHA Committee to Develop a Clinical Competence Statement on Electrocardiography and Ambulatory Electrocardiography) Endorsed by the International Society for Holter and Noninvasive Electrocardiology," Circulation, vol. 104, no. 25, 3169-3178, 2001.
  7. Corabian, Paula. "Accuracy and reliability of using computerized interpretation of electrocardiograms for routine examinations," Alberta Heritage Foundation for Medical Research, 2002.
  8. Paul, Joseph S., M. R. S. Reddy, and V. Jagadeesh Kumar, "Automatic detection of pvcs using autoregressive models," in Proc. of Engineering in Medicine and Biology Society, 1997. Proceedings of the 19th Annual International Conference of the IEEE., Vol. 1, 1997.
  9. Zhao, Lichu, Matt Wiggins, and George Vachtsevanos. "Premature ventricular contraction beat detection based on symbolic dynamics analysis," Proc. IASTED, 2003.
  10. Chiu, Chuang-Chien, Tong-Hong Lin, and Ben-Yi Liau, "Using correlation coefficient in ECG waveform for arrhythmia detection," Biomedical Engineering: Applications, Basis and Communications, vol. 17, no. 03, 147-152, 2005. https://doi.org/10.4015/S1016237205000238
  11. Inan, Omer T., Laurent Giovangrandi, and Gregory TA Kovacs, "Robust neural-network-based classification of premature ventricular contractions using wavelet transform and timing interval features," IEEE Transactions on Biomedical Engineering, vol. 53, no. 12, 2507-2515, 2006. https://doi.org/10.1109/TBME.2006.880879
  12. Christov, Ivalyo, and G. Bortolan, "Ranking of pattern recognition parameters for premature ventricular contractions classification by neural networks," Physiological Measurement, vol. 25, no. 5, 1281, 2004. https://doi.org/10.1088/0967-3334/25/5/017
  13. Wubbeler, Gerd, et al. "Verification of humans using the electrocardiogram," Pattern Recognition Letters, vol. 28, no. 10, 1172-1175, 2007. https://doi.org/10.1016/j.patrec.2007.01.014
  14. Israel, Steven A., et al, "ECG to identify individuals," Pattern recognition, vol. 38, no. 1, 133-142, 2005. https://doi.org/10.1016/j.patcog.2004.05.014
  15. Risk, Marcel0 R., Jamil F. Sobh, and J. Philip Saul, "Beat detection and classification of ECG using self organizing maps," in Proc. of Engineering in Medicine and Biology Society, 1997. Proceedings of the 19th Annual International Conference of the IEEE, vol. 1. IEEE, 1997.
  16. Afonso, Valtino X., et al, "ECG beat detection using filter banks," IEEE transactions on biomedical engineering, vol. 46, no. 2, 192-202, 1999.
  17. Cheng, W. T., and K. L. Chan, "Classification of electrocardiogram using hidden Markov models," in Proc. of Engineering in Medicine and Biology Society, Proceedings of the 20th Annual International Conference of the IEEE. IEEE, 1998.
  18. Inan, Omer T., Laurent Giovangrandi, and Gregory TA Kovacs, "Robust neural-network-based classification of premature ventricular contractions using wavelet transform and timing interval features," IEEE Transactions on Biomedical Engineering, vol. 53, no. 12, 2507-2515, 2006. https://doi.org/10.1109/TBME.2006.880879
  19. Shyu, Liang-Yu, Ying-Hsuan Wu, and Weichih Hu, "Using wavelet transform and fuzzy neural network for VPC detection from the Holter ECG," IEEE Transactions on Biomedical Engineering, vol. 51, no. 7, 1269-1273, 2004. https://doi.org/10.1109/TBME.2004.824131
  20. Hosseini, H. Gholam, K. J. Reynolds, and D. Powers, "A multi-stage neural network classifier for ECG events," in Proc. of Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE, vol. 2. IEEE, 2001.
  21. Senhadji, Lotfi, et al. "Comparing wavelet transforms for recognizing cardiac patterns." IEEE Engineering in Medicine and Biology Magazine, vol. 14, no. 2, 167-173, 1995. https://doi.org/10.1109/51.376755
  22. Christov, Ivaylo, et al, "Comparative study of morphological and time-frequency ECG descriptors for heartbeat classification," Medical engineering & physics, vol. 28, no. 9, 876-887, 2006. https://doi.org/10.1016/j.medengphy.2005.12.010
  23. de Chazal, Philip, and Richard B. Reilly, "Automatic classification of ECG beats using waveform shape and heart beat interval features," in Proc. of Acoustics, Speech, and Signal Processing, 2003. Proceedings.(ICASSP'03). 2003 IEEE International Conference on, vol. 2. IEEE, 2003.
  24. Y.H. Hu, S. Palreddy, W.J. Tompkins, "Patient-adaptable ECG beat classifier using a mixture of experts approach," IEEE Trans Biomed Eng, vol. 44, no. 9, pp. 891-897, 1997, https://doi.org/10.1109/10.623058
  25. Elham Dolatabadi and Serguei Primak, "Ubiquitous WBAN-based Electrocardiogram Monitoring System," in Proc. of Proceedings of IEEE Conference on e-Health Networking, Applications and Services, vol. 13, pp. 110-113, 2011,
  26. Pan, Jiapu, and Willis J. Tompkins, "A real-time QRS detection algorithm," IEEE transactions on biomedical engineering, vol. 3, 230-236, 1985.
  27. Le Cun, Yann, et al, "A theoretical framework for back-propagation," Proceedings of the 1988 Connectionist Models Summer School. CMU, Pittsburgh, Pa: Morgan Kaufmann, 1988.
  28. Goldberger, Ary L., et al, "Physiobank, physiotoolkit, and physionet," Circulation, vol. 101, no. 23, e215-e220, 2000. https://doi.org/10.1161/01.CIR.101.23.e215

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