Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Automatic Premature Ventricular Contraction Detection Using NEWFM

NEWFM을 이용한 자동 조기심실수축 탐지

  • Lim Joon-Shik (Department of E-Commerce Software, Kyungwon University)
  • 임준식 (경원대학교 전자거래학부)
  • Published : 2006.06.01
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Abstract

This paper presents an approach to detect premature ventricular contractions(PVC) using the neural network with weighted fuzzy membership functions(NEWFM). NEWFM classifies normal and PVC beats by the trained weighted fuzzy membership functions using wavelet transformed coefficients extracted from the MIT-BIH PVC database. The two most important coefficients are selected by the non-overlap area distribution measurement method to minimize the classification rules that show PVC classification rate of 99.90%. By Presenting locations of the extracted two coefficients based on the R wave location, it is shown that PVC can be detected using only information of the two portions.

본 논문은 가중 퍼지소속함수 기반 신경망(neural network with weighted fuzzy membership functions, NEWFM)을 이용하여 심전도(ECG) 신호로부터 조기심실수축(premature ventricular contractions, PVC)을 자동 탐지하는 방안을 제시하고 있다. NEWFM은 MIT-BIH 데이터베이스의 부정맥 심전도를 웨이블릿 변환(wavelet transform, WT)한 계수로부터 학습하여 정상 파형과 PVC 파형을 구분한다. 비중복면적 분산 측정법을 적용하여 중요도가 가장 높은 계수 2개를 추출하여 분류규칙을 최소화하였고, 이를 사용하여 99.90%의 PVC 분류성능을 나타내었다. 또한 추출된 두 계수의 R파를 기준으로 한 위치를 제시함으로써 두 위치의 정보만으로 PVC를 탐지할 수 있음을 보여주었다.

Keywords

References

  1. M. Engin, 'ECG beat classification using neurofuzzy network,' Pattern Recognition Letters 25, pp. 1715- 1722, 2004 https://doi.org/10.1016/j.patrec.2004.06.014
  2. F. M. Ham and Soowhan Han. 'Classification of Cardiac Arrhythmias Using Fuzzy ARTMAP', IEEE Trans. on Biomedical Engineering, VOL. 43, No.4, pp. 425-430, 1996 https://doi.org/10.1109/10.486263
  3. J. S. Lim, 'Finding Fuzzy Rules for IRIS by Neural Network with Weighted Membership Functions,' International Journal of Fuzzy Logic and Intelligent Systems, VOL. 4, No.2, pp. 211-216, Sep., 2004 https://doi.org/10.5391/IJFIS.2004.4.2.211
  4. J. S. Lim, D. Wang, Y-S. Kim, and S. Gupta, 'A neuro-fuzzy approach for diagnosis of antibody deficiency syndrome,' Neurocomputing 69, Issues 7-9, pp. 969-974, March 2006 https://doi.org/10.1016/j.neucom.2005.06.009
  5. J. S. Lim and S. Gupta, 'Feature Selection Using Weighted Neurc-Fuzzy Membership Functions,' The 2004 International Conference on Artificial Intelligence(lC-AI'04), June 21-24, 2004, VOL. 1, pp. 261-266, Las Vegas, Nevada, USA
  6. J. S. Lim, T-W Ryu, H-J Kim, and S. Gupta, 'Feature Selection for Specific Antibody Deficiency Syndrome by Neural Network with Weighted Fuzzy Membership Functions,' FSKD 2005 (LNCS 3614), pp. 811-820, Springer-Verlag, Aug. 2005
  7. 임준식,'가중 퍼지 소속함수 기반 신경망을 이용한 Wisconsin Breast Cancer 예측 퍼지규칙의 추출'. 한국정보처리학회, 제11-B권, 제 6호 pp.717-722,Oct., 2004 https://doi.org/10.3745/KIPSTB.2004.11B.6.717
  8. R. Mark and G. Moody, 'MIT-BIH arrhythmia database directory,' Mass. Inst. of Tech. (MIT), 1988
  9. K. Minami, H. Nakajima, and T. Toyoshima, 'Real-Time Discrimination of Ventricular Tachyarrhythrnia with Fourier-Transform Neural Network,' IEEE Trans. on Biomedical Engineering, VOL. 46, No.2, pp. 176-185, 1999
  10. T. H. Linh, S. Osowski, and M. Stodolski, 'On-Line Heart Beat Recognition Using Her8e Polynomials and Neuro-Fuzzy Networks,' IEEE Trans. on Instrumentation and Measurement, VOL. 52, No.4, pp. 1224-1231, 2003 https://doi.org/10.1109/TIM.2003.816841
  11. S. Osowski and T. H. Linh, 'ECG beat recognition using fuzzy hybrid neural network,' IEEE Trans. on Biomedical Engineering, VOL. 48, No. 4, pp. 1265-1271, 2001 https://doi.org/10.1109/10.959322
  12. C. Ramirez-Rodriguez and M. Hernandaz-Silveria, 'Multi-Thread Implementation of a Fuzzy Neural Network for Automatic ECG Arrhythmia Detection,' Proceedings in Computers m Cardiology 2001, pp. 297-300, Sept., 2001
  13. R. Silipo and C. Marchesi, 'Artificial Neural Networks for Automatic ECG Analysis,' IEEE Trans. on Signal Processing, VOL. 46, No.5, pp. 1417-1425, 1998 https://doi.org/10.1109/78.668803
  14. L.-Y Shyu, Y-H. Wu, and W. Hu, 'Using wavelet transform and fuzzy neural network for VPC detection from the holter ECG,' IEEE Transactions on Biomedical Engineering, VOL. 51, Issue 7, pp. 1269-1273, 2004 https://doi.org/10.1109/TBME.2004.824131