Journal of Internet Computing and Services (인터넷정보학회논문지)

Korean Society for Internet Information (한국인터넷정보학회)
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Extracting Fuzzy Rules for Classifying Ventricular Tachycardia/Ventricular Fibrillation Based on NEWFM

심실빈맥/심실세동 분류를 위한 NEWFM 기반의 퍼지규칙 추출

  • 신동근 (삼육대학교 컴퓨터학부) ;
  • 이상홍 (경원대학교 전자계산학과) ;
  • 임준식 (경원대학교 소프트웨어학부)
  • Published : 2009.04.30
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Abstract

This paper presents an approach to classify normal and Ventricular Tachycardia/Ventricular Fibrillation(VT/VF) from the Creighton University Ventricular Tachyarrhythmia DataBase(CUDB) using the neural network with weighted fuzzy membership functions(NEWFM). In the first step, wavelet transform is used for producing input values which are used in the next step. In the second step, two numbers of input features are extracted by phase space reconstruction method and peak extraction method using coefficients produced by wavelet transform in the previous step. NEWFM classifies normal and VT/VF beats using two numbers of input features, and then the accuracy rate is 90.13%.

본 논문은 가중 퍼지소속함수 기반 신경망(Neural Network with Weighted Fuzzy Membership Functions, NEWFM)을 이용하여 Creighton University Ventricular Tachyarrhythmia DataBase(CUDB)의 심전도(ECG) 신호로부터 정상리듬(Normal Sinus Rhythm, NSR)과 심실빈맥/심실세동(Ventricular Tachycardia/Ventricular Fibrillation, VT/VF)을 분류하는 방안을 제시하고 있다. NEWFM에서 사용할 특징입력을 추출하기 위해서 첫 번째 단계에서는 웨이블릿 변환(wavelet transform, WT)을 이용하였다. 두 번째 단계에서는 첫 번째 단계에서 생성된 웨이블릿 계수들을 위상공간 재구성(Phase Space Reconstruction, PSR)과 첨단(Peak) 추출 기법의 입력 값으로 이용하여 2개의 특징입력을 추출하였다. NEWFM은 이들 2개의 특징입력을 이용하여 정상리듬과 심실빈맥/심실세동을 분류하였고 그 결과로 90.13%의 분류성능을 나타내었다.

Keywords

References

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