The Transactions of the Korean Institute of Electrical Engineers D (대한전기학회논문지:시스템및제어부문D)

The Korean Institute of Electrical Engineers (대한전기학회)
권호 표지

A Study on the Detection of the Ventricular Fibrillation based on Wavelet Transform and Artificial Neural Network

웨이브렛과 신경망 기반의 심실 세동 검출 알고리즘에 관한 연구

  • 송미혜 (연세대 보건과학대 의공학과) ;
  • 박호동 (연세대 보건과학대 의공학과) ;
  • 이경중 (연세대 보건과학대 의공학과) ;
  • 박광리 (용인송담대 의료정보시스템과)
  • Published : 2004.11.01
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we proposed a ventricular fibrillation detection algorithm based on wavelet transform and artificial neural network. we selected RR intervals, the 6th and 7th wavelet coefficients(D6, D7) as features for classifying ventricular fibrillation. To evaluate the performance of the proposed algorithm, we compared the result of the proposed algorithm with that of fuzzy inference and fuzzy-neural network. MIT-BIH Arrhythmia database, Creighton University Ventricular Tachyarrhythmia database and MIH-BIH Malignant Ventricular Arrhythmia database were used as test and learning data. Among the algorithms, the proposed algorithm showed that the classification rate of normal and abnormal beat was sensitivity(%) of 96.10 and predictive positive value(%) of 99.07, and that of ventricular fibrillation was sensitivity(%) of 99.45. Finally. the proposed algorithm showed good performance compared to two other methods.

Keywords

References

  1. 윤형로, '실시간 부정맥 검출을 위한 컴퓨터 시스템에 관한 연구', 연세대학교 박사학위논문, 1985
  2. H. X. Zhang, Y. S. Zhu, Z. M. Wang, 'Complexity measure and complexity rate information based detection of ventricular tachycardia and fibrillation', Medical & Biological Engineering & Computing, Vol. 38, pp. 553-557, 2000 https://doi.org/10.1007/BF02345752
  3. V. X. Afonso, W. J. Tompkins, 'Detection Ventricular Fibrillation', IEEE Engineering in Medicine and Biology Society, pp. 152-159, 1995 https://doi.org/10.1109/51.376752
  4. M. I. Oiws, A. H. Abou-Zied, A. M. Youssef, 'Study of Features based on Nonlinear Dynamical, Modeling in ECG Arrhythmia Detection and Classification', IEEE Transactions on Biomedical Engineering, Vol. 49, No.7, pp. 733-736, 2002 https://doi.org/10.1109/TBME.2002.1010858
  5. N. Srinivasan, M.T. Wong, S.M. Krishnan, 'A new Phase Space Analysis Algorithm for Cardiac Arrhythmia Detection', 2003 https://doi.org/10.1109/IEMBS.2003.1279515
  6. K. I. Minami, H. Nakajima, 'Real-Time Discrimination of Ventricular Tachyarrhythmia with Fourier-Transform Neural Network', IEEE Transactions on Biomedical Engineering, Vol. 46, No.2, pp. 179-185, 1999 https://doi.org/10.1109/10.740880
  7. T. Sugiura, H. Hirata, Y. Harada, T. Kazui, 'Automatic Discrimination of Arrhythmia Waveforms using Fuzzy Logic', Proceeding of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vol. 20, No.1, pp. 108-111, 1998 https://doi.org/10.1109/IEMBS.1998.745839
  8. Minami, K., Ohkuma, Y., Nakajima, H., Toyoshima, T., 'Real-time ventricular arrhythmia detection with Fourier analysis and neural network', Computers in Cardiology, pp. 545-548, 1996 https://doi.org/10.1109/CIC.1996.542594
  9. H. Gholam-Hosseini, H. Nazeran, K. J. Reynolds., 'ECG noise cancellation using digital filters', 2nd International Conference on Bioelectromagnetism, pp. 151-152, 1998 https://doi.org/10.1109/ICBEM.1998.666440
  10. 이승하, '시-주파수 공간 분석법을 이용한 심실 세동의 자동 검출에 관한 연구,' 연세대학교 석사학위논문, pp. 24-27, 1999
  11. R. H. Clayton, A. Murray, R. W.F. Campbell, 'Comparison of four techniques for recognition of ventricular fibrillation from the surface ECG' , Medical & Biological Engineering & Computing, pp. 111-117, 1993 https://doi.org/10.1007/BF02446668
  12. 최진호, '초보자를 위한 심전도 가이드 : chapter 5 concept of arrhythmia', 2000.3.9. 제4판, 2000.3.23. 개정판
  13. Yang Wang, Yi-Sheng Zhu, Nitish V. Thakor, Yu-Hong Xu, 'A short-Time Multifractal Approach for Arrhythmia Detection Based on Fussy Neural Network', IEEE transactions on Biomedical Engineering, Vol. 48, No.9, 2001 https://doi.org/10.1109/10.942588
  14. J. H, T. Wang, B. W, 'Using FNN for classification of cardiac arrhythmia', IEEE Conference on Computers, Communications, Control and Power Engineering, Vol. 1, pp. 687-690, 2002
  15. L. Y. Shyu, Y. H. Wu, W. Hu, 'Using Wavelet Transform and Fuzzy Neural Network for VPC Detection From the Holter ECG', IEEE Transactions on Biomedical Engineering, Vol. 51, No.7, pp. 1269-1273, 2004 https://doi.org/10.1109/TBME.2004.824131
  16. 박광리, '스트레스 심전도의 잡음 제거를 위한 WAF와 WIF의 설계', 연세대학교 박사학위논문, 2000
  17. Y. K. Jung, W. J. Tompkins, 'Detecting and classifying Life-Thresatening ECG Ventricular Arrhythmia using wavelet Decomposition', 2003 https://doi.org/10.1109/IEMBS.2003.1280397
  18. R. H. Clayton, A. Murray, 'Comparison of techniques for time-frequency analysis of the ECG during human ventricular fibrillation', 1998 https://doi.org/10.1049/ip-smt:19982322
  19. Xu-Sheng Zhang, Yi-Sheng Zhu, V. Thakor, Zhi-Zhong Wang, 'Detecting Ventricular Tachycardia and Fibrillation by Complexity Measure', 1999 https://doi.org/10.1109/10.759055
  20. S. H. Lee, 'Detection of Ventricular Fibrillation using Time-Frequency Analysis', 1999
  21. N. Srinivasan, M.T. Wong, S.M. Krishnan, 'A new Phase Space Analysis Algorithm for Cardiac Arrhythmia Detection', 2003 https://doi.org/10.1109/IEMBS.2003.1279515