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Neuro-Fuzzy Modelling of Heart Rate Signals and Application to Diagnostics

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Fuzzy Systems in Medicine

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 41))

Abstract

Heart rate variability (HRV) is examined by methods combining neural networks and fuzzy logic. Multiple features are extracted from examples of heart rate data from normal adult subjects, subjects recently suffering from a heart attack, subjects with a history of ischaemic heart disease undergoing a coronary investigation, and subjects in atrial fibrillation. Special attention is given to the analysis of fractal features extracted from heart rate sequences. The methodologies of fuzzy neural networks (FuNN) and evolving fuzzy neural networks (EFuNN) are described and applied to heart rate variability. A description of applications of heart rate variability analysis in medicine is given. The proposed methods can be used for further practical applications in this area.

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References

  1. Akselrod, S., Gordon, D., Ubel, F. A., Shannon, D. C., Berger, A. C., Cohen, R. J. (1981): Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control. Science, 213, 220–222.

    Article  PubMed  CAS  Google Scholar 

  2. Alexopoulos, D., Yusuf, S., Johnston, J. A., Bostock, J., Sleight, P., Yacoub, M. H. (1988): The 24-hour heart rate behavior in long-term survivors of cardiac transplantation. Am J Cardiol, 61, 880–884.

    Article  PubMed  CAS  Google Scholar 

  3. Alga, A., Tijssen, J. G., Roelandt, J. R, Pool, J., Lubsen, J. (1993): Heart rate variability from 24-hour electrocardiography and the 2-year risk for sudden death. Circulation, 88, 180–185.

    Article  Google Scholar 

  4. Amari, S. and Kasabov, N. eds (1998): Brain-like computing and intelligent information systems,Springer Verlag.

    Google Scholar 

  5. Bernardi, L., Salvucci, F., Suardi, R., et al. (1990): Evidence for an intrinsic mechanism regulating heart rate variability in the transplanted and the intact heart during submaximal dynamic exercise? Cardiovasc Res, 24, 969–981.

    Article  PubMed  CAS  Google Scholar 

  6. Bigger, J. T., Jr., Fleiss, J. L., Steinman, et al, (1992): Correlations among time and frequency domain measures of heart period variability two weeks after acute myocardial infarction. Am J Cardiol, 69, 891–898.

    Article  PubMed  Google Scholar 

  7. Bigger, J. T., Jr., Fleiss, J. L., Steinman, R C., Rolnitzky, L. M., Kleiger, R E., Rottman, J. N. (1992): Frequency domain measures of heart period variability and mortality after myocardial infarction. Circulation, 85, 164–171.

    Article  PubMed  Google Scholar 

  8. Carpenter, G. and Grossberg, S., (1991): Pattern recognition by self-organizing neural networks, the MIT Press, Cambridge, MA.

    Google Scholar 

  9. Casolo, G., Balli, E., Taddei, T., Amuhasi, J., Gori, C. (1989): Decreased spontaneous heart rate variability in congestive heart failure. Am J Cardiol, 64, 1162–1167.

    Article  PubMed  CAS  Google Scholar 

  10. Chaffin, D. G., Goldberg, C. C., Reed, K. L. (1991): The dimension of chaos in the fetal heart rate. Am J Obstet Gynecol, 165, 1425–1429.

    PubMed  CAS  Google Scholar 

  11. Dawes, G. S., Visser, G. H., Goodman, J. D., Redman, C. W. (1981): Numerical analysis of the human fetal heart rate: the quality of ultrasound records. Am J Obstet Gynecol, 141, 43–52.

    PubMed  CAS  Google Scholar 

  12. Dougherty, C. M., Burr, R. L. (1992): Comparison of heart rate variability in survivors and nonsurvivors of sudden cardiac arrest. Am J Cardiol, 70, 441–448.

    Article  PubMed  CAS  Google Scholar 

  13. Ewing, D. J., Campbell, I. W., Clarke, B. F. (1980): The natural history of diabetic autonomic neuropathy. Q JMed., 49, 95–108.

    CAS  Google Scholar 

  14. Ewing, D. J., Neilson, J. M., Travis, P. (1984): New method for assessing cardiac parasympathetic activity using 24 hour electrocardiograms. Br Heart J, 52, 396–402.

    Article  PubMed  CAS  Google Scholar 

  15. Ewing, D. J., Marlyn, C. N., Young, R. J., Clarke, B. F. (1985): The value of cardiovascular autonomic function tests: 10 years experience in diabetes. Diabetes Care, 8, 491–498.

    Article  PubMed  CAS  Google Scholar 

  16. Felgueiras, C. S., Marques de Sâ, J. P., Bernardes, J., Gama, S. (1998): Classification of foetal heart rate sequences based on fractal features. Med. Biol. Eng. Comput., 36, 197–201

    Article  PubMed  CAS  Google Scholar 

  17. Goldberger, A. L. (1996): Non-linear dynamics for clinicians: chaos theory, fractals, and complexity at the bedside. Lancet, 347, 1312–1314.

    Article  PubMed  CAS  Google Scholar 

  18. Guzzetti, S., Signorini, M. G., Cogliati, C., Mezzetti, S., Porta, A., Cerotti, S., Malliani, A. (1996): Non-linear dynamics and chaotic indices in heart rate variability of normal subjects and heart-transplanted patients. Cardiovasc Res, 31, 441–446.

    PubMed  CAS  Google Scholar 

  19. Higuchi, T. (1988): Approach to an irregular time series on the basis of fractal theory. Physica D, 31, 277–283.

    Article  Google Scholar 

  20. Ho, K. K., Moody, G. B., Peng, C. K., Mietus, J. E., Larson, M. G., Levy, D., Goldberger, A. L. (1997): 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, 842–848.

    Article  PubMed  CAS  Google Scholar 

  21. Huikuri, H. V., Linnaluoto, M. K., Seppanen, T., Airaksinen, K. E., Kessler, K. M., Takkunen, J. T., Myerburg, R J. (1992): Circadian rhythm of heart rate variability in survivors of cardiac arrest. Am J Cardiol, 70, 610–615.

    Article  PubMed  CAS  Google Scholar 

  22. Huikuri, H. V., Valkama, J. O., Airaksinen, K. E., Seppanen, T., Kessler, K. M., Takkunen, J. T., Myerburg, R. J. (1993): Frequency domain measures of heart rate variability before the onset of nonsustained and sustained ventricular tachycardia in patients with coronary artery disease. Circulation, 87, 1220–1228.

    Article  PubMed  CAS  Google Scholar 

  23. Jong, R. (1993) ANFIS: adaptive network-based fuzzy inference system, IEEE Trans. on Syst.,Man, Cybernetics, 23(3), May-June, 665–685.

    Google Scholar 

  24. Kasabov, N. (1998): ECOS: A framework for evolving connectionist systems and the eco learning paradigm, Proceedings of ICONIP’98, Kitakyushu, Japan, Oct98, IOSPress, 1232–35.

    Google Scholar 

  25. Kasabov, N. (1999): Evolving connectionist and fuzzy connectionist system for on-line decision making and control, in: Soft Computing in Engineering Design and Manufacturing, Springer Verlag.

    Google Scholar 

  26. Kasabov, N. (1999): Evolving connectionist and fuzzy-connectionist systems: theory and applications for adaptive, on-line intelligent systems, in: N.Kasabov and R.Kozma (eds) Neuro-fuzzy Tools and Techniques for Intelligent Systems, Springer Verlag (Physica Verlag).

    Google Scholar 

  27. Kasabov, N. (1998): Evolving fuzzy neural networks–algorithms, applications and biological motivation, in Proceedings of lizuka’98, Iizuka, Japan, pp. 271–274

    Google Scholar 

  28. Kasabov, N. (1996) Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering, The MIT Press, Cambridge, MA.

    Google Scholar 

  29. Kasabov, N. (1996): Learning fuzzy rules and approximate reasoning in fuzzy neural networks and hybrid systems, Fuzzy Sets and Systems, 82 (2) 220.

    Google Scholar 

  30. Kasabov, N., Kim J S, Watts, M., Gray, A (1997) FuNN/2- A Fuzzy Neural Network Architecture for Adaptive Learning and Knowledge Acquisition, Information Sciences -Applications, 101 (3–4): 155–175.

    Article  Google Scholar 

  31. Kleiger, R. E., Miller, J. P., Bigger, J. T., Jr., Moss, A. J. (1987): Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. Am J Cardiol, 59, 256–262.

    Article  PubMed  CAS  Google Scholar 

  32. Kohonen, T. (1990) The Self-Organizing Map. Proceedings of the IEEE, vol.78, N-9, pp. 1464–1497.

    Google Scholar 

  33. Kohonen, T. (1997): Self-Organizing Maps,second edition, Springer Verlag.

    Google Scholar 

  34. Kozma, R, Swope, J. A., Kasabov, N. K., Williams, M. J. A. (1997): Multiagent implementation of fractal analysis by fuzzy neural networks. Proc.Int.ConfNeural Proc. and Intelligent Inf. Syst. ICONIP’97, Dunedin, SpringerVerlag, Sing., 162–5.

    Google Scholar 

  35. Luczak, H.,Laurig,W. (1973): An analysis of heart rate variability. Ergonomics, 16, 85–97.

    Article  Google Scholar 

  36. Malik, M., Farrell, T., Cripps, T., Caimn, A. J. (1989): Heart rate variability in relation to prognosis after myocardial infarction: selection of optimal processing techniques. Eur Heart J, 10, 1060–1074.

    PubMed  CAS  Google Scholar 

  37. Mandelbrot, B. B. (1983): The Fractal Geometry of Nature, W.H. Freeman and Company, New York.

    Google Scholar 

  38. Nolan, J., et al (1992): Decreased cardiac parasympathetic activity in chronic heart failure and its relation to left ventricular function. Br Heart J, 67, 482–485.

    Article  PubMed  CAS  Google Scholar 

  39. Odemuyiwa, O., et al (1991): Comparison of the predictive characteristics of heart rate variability index and left ventricular ejection fraction for all-cause mortality, arrhythmic events and sudden death after acute myocardial infarction. Am J Cardiol, 68, 434–439.

    Article  PubMed  CAS  Google Scholar 

  40. Ohno-Machado, L., Musen, M. A. (1997): Sequential versus standard neural networks for pattern recognition: an example using the domain of coronary heart disease. Computers in Biology and Medicine, 27 (4), 267–281.

    Article  PubMed  CAS  Google Scholar 

  41. Pagani, A. et al (1986): Power spectral analysis of heart rate and arterial pressure variabilities as a marker of sympatho-vagal interaction in man and conscious dog. Circ Res, 59, 178–193.

    Article  PubMed  CAS  Google Scholar 

  42. Pal, N. R. (1998): Connectionist approaches for feature analysis. Machine Intelligence Unit, Indian Statistical Institute, Calcutta.

    Google Scholar 

  43. Pomeranz, B., et al. (1985): Assessment of autonomic function in humans by heart rate spectral analysis. Am J Physiol, 248, H151–153.

    PubMed  CAS  Google Scholar 

  44. Sands, K. E., et al (1989): Power spectrum analysis of heart rate variability in human cardiac transplant recipients. Circulation, 79, 76–82.

    Article  PubMed  CAS  Google Scholar 

  45. Sayers, B. M. (1973): Analysis of heart rate variability. Ergonomics, 16, 1732.

    Google Scholar 

  46. Schwartz, P. J., Stramba-Badiale, M., Segantini, A., Austori, P., Bosi, G., Giorgetti, R., Grancini, F., Mami, E. D., Perticone, F., Rosti, D., Salice, P. (1998): Prolongation of the QT interval and the sudden infant death syndrome. NEngl JMed, 338, 1709–1714.

    Article  CAS  Google Scholar 

  47. Stein, K. M., Borer, J. S., Hochreiter, C., Devereux, R. B., Kligfield, P. (1994): Variability of the ventricular response in atrial fibrillation and prognosis in chronic nonischemic mitral regurgitation. Am J Cardiol, 74, 906–911.

    Article  PubMed  CAS  Google Scholar 

  48. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology (1996): Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Eur Heart J 17, 354–381.

    Article  Google Scholar 

  49. van den Berg, M. P., Haaksma, J., Brouwer, J., Tieleman, R. G., Mulder, G., Crijns, H. J. (1997): Heart rate variability in patients with atrial fibrillation is related to vagal tone. Circulation, 96, 1209–1216.

    Article  PubMed  Google Scholar 

  50. Voss, A., Kurths, J., Kleiner, H.J., Witt, A., Wessel, N. (1995): Improved analysis of heart rate variability by methods of nonlinear dynamics. Journal of Electrocardiology, 28, Supplement, 81–88.

    Google Scholar 

  51. Wolf, M. M., Varigos, G. A., Hunt, D., Sloman, J. G. (1978): Sinus arrhythmia in acute myocardial infarction. Med JAust, 2, 52–53.

    CAS  Google Scholar 

  52. Yamamoto,Y., Hughson, R. L. (1994): On the fractal nature of heart rate variability in humans: effects of data length and f3-adrenergic blockade. Am JPhysiol, 266, R40 - R49.

    CAS  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer and Information Science, University of Otago, P.O. Box 56, Dunedin, New Zealand

    J. A. Swope & N. K. Kasabov

  2. Department of Medicine, University of Otago, P.O. Box 913, Dunedin, New Zealand

    M. J. A. Williams

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  1. J. A. Swope
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  2. N. K. Kasabov
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  3. M. J. A. Williams
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Editor information

Editors and Affiliations

  1. Institute of Computer Science, Technical University of Łódź, ul. Sterlinga 16/18, 90-217, Łódź, Poland

    Piotr S. Szczepaniak PhD, DSc (Associate Professor) (Associate Professor)

  2. School of Computing and Mathematical Science, Liverpool John Moores University, L33AF, Liverpool, UK

    Paulo J. G. Lisboa

  3. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk

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© 2000 Springer-Verlag Berlin Heidelberg

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Swope, J.A., Kasabov, N.K., Williams, M.J.A. (2000). Neuro-Fuzzy Modelling of Heart Rate Signals and Application to Diagnostics. In: Szczepaniak, P.S., Lisboa, P.J.G., Kacprzyk, J. (eds) Fuzzy Systems in Medicine. Studies in Fuzziness and Soft Computing, vol 41. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1859-8_25

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  • DOI: https://doi.org/10.1007/978-3-7908-1859-8_25

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-662-00395-4

  • Online ISBN: 978-3-7908-1859-8

  • eBook Packages: Springer Book Archive

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