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


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.


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