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.


Heart Rate Variability(HRV);Time Domain;Frequency Domain;Short Term HRV Analysis;Myocardial Ischemia(MI)


Supported by : 한국학술진흥재단


  1. N. A. Agadzhanyan, T. E. Batotsyrenova, A. E. Severin, "Comparison of specific features of the heart rate variability in students living in regions with different natural and climatic conditions," HUMAN PHYSIOLOGY, Vol.33, No.6, pp.715-719, 2007.
  2. M. Malik, "Heart rate variability - standards of measurement, physiological interpretation, and clinical use," Circulation, pp.1043-1065, 1996.
  3. J. Mateo and P. Laguna, "Improved heart rate variability signal analysis from the beat occurrence times according to the IPFM model," IEEE Trans. Biomed. Eng., Vol.47, No.8, pp.985-996, 2000.
  4. R. DeBoer, J. Karemaker, and J. Strackee, "Comparing spectra of a series of point events particularly for heart rate variability data," IEEE Trans. Biomed. Eng., Vol.31, No.4, pp.384-387, 1984.
  5. A. Subasi, "Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients," Expert Systems with Applications, Vol.28, Issue 4, pp.701-711, 2005.
  6. M. W. Zimmerman and R. J. Povinelli, "On improving the classification of myocardial ischemia using holter ECG data," Computers in Cardiology, pp.377-380, 2004.
  7. A. Taddei, A. Biagini, and G. Distante, "The European ST-T database: development, distribution and use," in Proc. Computers in Cardiology, pp.177-180, 1990(9).
  8. A. Gharaviri, M. Teshnehlab, and H. A. Moghaddam, "Ischemia detection via ECG using ANFIS, " in Proc. 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.1163-1166, 2008(8).
  9. K.-M. Chang, Z.-Z. Lin, S.-H. Liu. and C.-C. Tyan, "Myocardial Ischemia Detection by Pulse Signal Features and Fuzzy Clustering," In Proc. International Conference on BioMedical Engineering and Informatics, Vol.2, pp.473-477, 2008(5)
  10. M. W. Zimmerman and R. J. Povinelli, "On improving the classification of myocardial ischemia using holter ECG data," in Proc. Computers in Cardiology, pp.377-380, 2004(9).
  11. J. S. Lim, "Finding features for real-time premature ventricular contraction detection using a fuzzy neural network system," IEEE Trans. on Neural Networks, Vol.20, issue 3, pp.522-527, 2009.
  12. Z. X. Zhang, S. H. Lee, and J. S. Lim, "Comparison of Feature Selection Methods in ECG Signal Classification," in Proc. The 4th International Conference on Ubiquitous Information Management and Communication, pp.502-506, 2010(1).
  13. J S. Lim, D. Wang, Y.-S. Kim, and S. Gupta, "A neuro-fuzzy approach for diagnosis of antibody deficiency syndrome," Neurocomputing, Vol.69, issues 7-9, pp.969-974, 2006.
  14. R. Mark and G. Moody, "The MIT-BIH Normal Sinus Rhythm Database Directory," Mass. Inst. of Tech. (MIT), 1988.
  15. S. Cerutti, L. Mainardi, A Bianchi, M. G. Signorini, and M. Bertinelli, "Time-variant autoregressive spectral estimation in acute ischemic episodes," in Proc. Computers in Cardiology, pp. 315-318, 1992(10).
  16. M. Malik, "Heart rate variability standards of measurement, physiological interpretation, and clinical use," Circulation, Vol.93, pp.1043-1065, 1996.
  17. B. M. Asl, S. K. Setarehdan, and M. Mohebbi, "Support Vector Machine-Based Arrhythmia Classification Using Reduced Features of Heart Rate Variability Signal," Artificial Intelligence in Medicine, Vol.44, No.1, 2008.
  18. E. R Migliaro, R. Canetti, and P. Contreras, "Short-Term Studies of Heart Rate Variability: Comparison of Two Methods for Recording," Physiological Measurement, Vol.25, No.6, pp.15-20, 2004.
  19. A. Kandaswamy, C. S. Kumar, R. P. Ramanathan, S. Jayaraman, and N. Malmurugan, "Neural Classification of Lung Sounds Using Wavelet Coefficients," Computers in Biology and Medicine, Vol.34, No.6, pp.523-537, 2004.
  20. S. H. Lee and J. S. Lim, "Forecasting KOSPI based on a neural network with weighted fuzzy membership functions," Expert Systems with Applications, Vol.38, Issue 4, pp.4259-4263, 2011.
  21. S. H Lee and J. S. Lim, "Minimized Stock Forecasting Features Selection by Automatic Feature Extraction Method, Korean Institute Intelligent Systems, Vol.19, Issue 2, pp.206-211, 2009.