Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Pattern Classification for Biomedical Signal using BP Algorithm and SVM

BP알고리즘과 SVM을 이용한 심전도 신호의 패턴 분류

  • 김만선 (한국표준과학연구원 인간정보그룹) ;
  • 이상용 (공주대학교 컴퓨터공학과)
  • Published : 2004.02.01
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Abstract

ECG consists of various waveforms of electric signals of heat. Datamining can be used for analyzing and classifying the waveforms. Conventional studies classifying electrocardiogram have problems like extraction of distorted characteristics, overfitting, etc. This study classifies electrocardiograms by using BP algorithm and SVM to solve the problems. As results, this study finds that SVM provides an effective prohibition of overfitting in neural networks and guarantees a sole global solution, showing excellence in generalization performance.

심전도 데이터는 심장의 전기적인 신호의 다양한 파형으로 이루어지며, 이와 같은 파형을 분석하고 분류하기 위하여 데이터마이닝 기법을 이용할 수 있다. 심전도신호를 분류하기 위한 기존의 연구들은 왜곡된 특징추출과 과적합 등 문제점을 가지고 있다. 본 연구에서는 이와 같은 문제점들을 해결하기 위하여 BP 알고리즘과 SVM을 이용하여 심전도 신호를 분류해 보았다 그 결과 SVM이 신경망에서 발생하는 과적합을 효과적으로 방지하고, 유일한 전역해를 보장함으로써, 일반화 성능에서 우수함을 보이고 있다는 사실을 확인하였다.

Keywords

References

  1. www.supportvector.ws/html/downloads.html
  2. Z. Dokur and T. Olmez, "ECG beat cia ssification by a novel hybrid neural network", Computer Methods and Programs in Biomedicine, Volume 66, Issues 2-3, pp. 167 181, September 2001. https://doi.org/10.1016/S0169-2607(00)00133-4
  3. P.M. Rautaharju, S.H. Zhou, E.W. Hancock, B. Milan Hor[aacute]fecaronJek, Dirk Q.Feild, James M. Lindauer, Galen S. Wagner, O. Pahlm and Charles L.Feldman, "Comparability of 12-lead ECGs derived from EASI leads with standard 12-lead ECGS in the classification of acute myocardial ischemia and old myocardial infarction", Journal of Electrocardiology, Volume 35, Issue 4, Part 2, pp. 35-39, October 2002.
  4. U. Rajendra Acharya, P. Subbanna Bhat, S. S.Iyengar, A. Rao and S.Dua, "Classification of heart rate data using artificial neural network and fuzzy equivalence relation, Pattern Hecognition", Volume 36, Issue 1. pp. 61-68, January 2003). https://doi.org/10.1016/S0031-3203(02)00063-8
  5. B. HEDEN, "Agreement Between Artificial Neural Networks and Experienced Electro cardiographer on Electrocardiographic Diagnosis of Healed Myocardial Infarction", JACC, Vol.28, No.4, pp.1012-1016, October 1996. https://doi.org/10.1016/S0735-1097(96)00269-0
  6. R. Silipo, M. Goru, A. Taddei, M. Varanini, C. Marchesi, "Classification of Arrhythmic Events in Ambulatory Electrocardiogram, Using Artificial Neural Networks", Computers and Biomedical research Vol.28, pp. 305-318, 1995. https://doi.org/10.1006/cbmr.1995.1021
  7. K. Sternickel, "Automatic patttern recognition in ECG time series", Computer Methods and Programs in Biomedicine, Vol.68, pp. 109-115, 2002. https://doi.org/10.1016/S0169-2607(01)00168-7
  8. http://www.kernel-machines.org.

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