Journal of Internet Computing and Services (인터넷정보학회논문지)

Korean Society for Internet Information (한국인터넷정보학회)
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Detection of Arrhythmia Using Heart Rate Variability and A Fuzzy Neural Network

심박수 변이도와 퍼지 신경망을 이용한 부정맥 추출

  • 장형종 (경원대학교 전자계산학) ;
  • 임준식 (경원대학교 컴퓨터소프트웨어)
  • Published : 2009.10.30
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Abstract

This paper presents an approach to detect arrhythmia using heart rate variability and a fuzzy neural network. The proposed algorithm diagnoses arrhythmia using 32 RR-intervals that are 25 seconds on average. We extract six statistical values from the 32 RR-intervals, which are used to input data of the fuzzy neural network. This paper uses the neural network with weighted fuzzy membership functions(NEWFM) to diagnose arrhythmia. The NEWFM used in this algorithm classifies normal and arrhythmia. The performances by Tsipouras using the 48 records of the MIT-BIH arrhythmia database was below 80% of SE(sensitivity) and SP(specificity) in both. The detection algorithm of arrhythmia shows 88.75% of SE, 82.28% of SP, and 86.31% of accuracy.

본 논문에서는 심전도 신호로부터 부정맥을 진단하는 방법으로 심박수 변이도와 퍼지 신경망을 이용하는 방안을 제시하고 있다. 제안한 부정맥 진단 알고리즘은 32개 RR 간격의 심박수 변이도, 즉 평균 25초 내외의 심박수 변화를 이용하여 부정맥을 진단하는 알고리즘이다. 부정맥 진단 알고리즘은 32개 RR 간격을 이용하여, 통계적 특징 6개를 추출한 후, 가중 퍼지소속함수 기반 신경망으로 학습하여 정상 구간과 부정맥 구간을 분류한다. 부정맥 진단 알고리즘은 Tsipouras 논문군(48개 레코드)에서 SE와 SP 각각 80% 이하의 성능을 보이는 기존연구와는 달리, SE는 88.75%, SP는 82.28%, 전체 분류율은 86.31%의 신뢰성 있는 결과를 나타낸다.

Keywords

References

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