Detection and Classification of ECG Chaotic Components Using ANN Trained by Specially Simulated Data

  • Polina Kurtser
  • Ofer Levi
  • Vladimir Gontar
Part of the Communications in Computer and Information Science book series (CCIS, volume 311)


This paper presents the use of simulated ECG signals with known chaotic and random noise combination for training of an Artificial Neural Network (ANN) as a classification tool for analysis of chaotic ECG components. Preliminary results show about 85% overall accuracy in the ability to classify signals into two types of chaotic maps – logistic and Henon. Robustness to random noise is also presented. Future research in the form of raw data analysis is proposed, and further features analysis is needed.


ECG Deterministic chaos Artificial Neural Networks 


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  1. 1.
    Guevara, M.R., Glas, L., Shrier, A.: Phase locking, period-doubling bifurcations, and irregular dynamics in periodically stimulated cardiac cells. Science 214, 1350–1353 (1981)CrossRefGoogle Scholar
  2. 2.
    Voss, A., Schulz, S., Schroeder, R., Baumert, M., Caminal, P.: Methods derived from nonlinear dynamics for analysing heart rate variability. Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences 367(1887), 277–296 (2009)zbMATHCrossRefGoogle Scholar
  3. 3.
    Guzzetti, S., Signorini, M.G., Cogliati, C., Mezzetti, S., Porta, A., Cerutti, S., Malliani, A.: Non-linear dynamics and chaotic indices in heart rate variability of normal subjects and heart-transplanted patients. Cardiovascular 6363(95), 441–446 (1996)Google Scholar
  4. 4.
    Loskutov, A.: Time series analysis of ECG: a possibility of the initial diagnostics. International Journal of Bifurcation and Chaos 17(10), 3709–3713 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Ho, K.K.L., Moody, G.B., Peng, C.K., Mietus, J.E., Larson, M.G., Levy, D., Goldberger, A.L.: Predicting survival in heart failure case and control subjects by use of fully automated methods for deriving nonlinear and conventional indices of heart rate dynamics. Circulation 96(3), 842–848 (1997)CrossRefGoogle Scholar
  6. 6.
    Abibullaev, B., Seo, H.D.: A new QRS detection method using wavelets and artificial neural networks. Journal of Medical Systems 35(4), 683–691 (2011)CrossRefGoogle Scholar
  7. 7.
    Owis, M.I., Abou-Zied, A.H., Youssef, A.-B.M., Kadah, Y.M.: Study of features based on nonlinear dynamical modeling in ECG arrhythmia detection and classification. IEEE Transactions on Biomedical Engineering 49(7), 733–736 (2002)CrossRefGoogle Scholar
  8. 8.
    Übeyli, E.D.: Detecting variabilities of ECG signals by Lyapunov exponent. Neural Computing and Applications 18(7), 653–662 (2009)CrossRefGoogle Scholar
  9. 9.
    Mitschke, F., Dämmig, M.: Chaos versus noise in experimental data. International Journal of Bifurcation and Chaos 3, 693–702 (1993)zbMATHCrossRefGoogle Scholar
  10. 10.
    Govindan, R.B., Narayanan, K., Gopinathan, M.S.: On the evidence of deterministic chaos in ECG: Surrogate and predictability analysis. Chaos 8(2), 495–502 (1998)zbMATHCrossRefGoogle Scholar
  11. 11.
    Gothwal, H., Kedawat, S., Kumar, R.: Cardiac arrhythmias detection in an ECG beat signal using fast fourier transform and artificial neural network. Journal of Biomedical Science and Engineering 4(4), 289–296 (2011)CrossRefGoogle Scholar
  12. 12.
    Dokur, Z., Olmez, T.: Comparison of discrete wavelet and Fourier transforms for ECG beat classification. Electronics Letters 35(18), 1502–1504 (1999)CrossRefGoogle Scholar
  13. 13.
    Bigger, J.T., Fleiss, J.L., Steinman, R.C., Rolnitzky, L.M., Schneider, W.J., Stein, P.K.: RR variability in healthy, middle-aged persons compared with patients with chronic coronary heart disease or recent acute myocardial infarction. Circulation 91(7), 1936–1943 (1995)CrossRefGoogle Scholar
  14. 14.
    Karrakchou, M., Vibe-Rheymer, K., Vesin, J.M., Pruvot, E., Kunt, M.: Improving cardiovascular monitoring through modern techniques. IEEE Engineering in Medicine and Biology Magazine 15(5), 68–78 (1996)CrossRefGoogle Scholar
  15. 15.
    Losada, R.: ECG. 1988-2002 The MathWorks, Inc.Google Scholar
  16. 16.
    McSharry, P., Clifford, G.: A dynamical model for generating synthetic electrocardiogram signals. IEEE Transactions on Biomedical Engineering 50(3), 289–294 (2003)CrossRefGoogle Scholar
  17. 17.
    Goldberger, L.A., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E.: Resource for Complex Physiologic Signals PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research. Circulation 101(23), e215–e220 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Polina Kurtser
    • 1
  • Ofer Levi
    • 1
  • Vladimir Gontar
    • 1
  1. 1.Department of Industrial Engineering and ManagementBen-Gurion University of the NegevBeer-ShevaIsrael

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