Comparison of HRV Time and Frequency Domain Features for Myocardial Ischemia Detection

심근허혈검출을 위한 심박변이도의 시간과 주파수 영역에서의 특징 비교

  • Received : 2011.01.11
  • Accepted : 2011.03.15
  • Published : 2011.03.28


Heart Rate Variability (HRV) analysis is a convenient tool to assess Myocardial Ischemia (MI). The analysis methods of HRV can be divided into time domain and frequency domain analysis. This paper uses wavelet transform as frequency domain analysis in contrast to time domain analysis in short term HRV analysis. ST-T and normal episodes are collected from the European ST-T database and the MIT-BIH Normal Sinus Rhythm database, respectively. An episode can be divided into several segments, each of which is formed by 32 successive RR intervals. Eighteen HRV features are extracted from each segment by the time and frequency domain analysis. To diagnose MI, the Neural Network with Weighted Fuzzy Membership functions (NEWFM) is used with the extracted 18 features. The results show that the average accuracy from time and frequency domain features is 75.29% and 80.93%, respectively.


Supported by : 한국학술진흥재단


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