Advertisement

Fundamental Analyses of Ventricular Fibrillation Signals by Parametric, Nonparametric, and Dynamical Methods

  • Nitish V. Thakor
  • Ahmet Baykal
  • Aldo Casaleggio

Abstract

Ventricular fibrillation (VF) is the malignant electrical rhythm of the heart. Fundamental understanding of this rhythm can only be obtained by considering signals generated by single heart cells, isolated heart tissue, and the whole organ. Interpretation of these complex phenomena also requires that we employ the modern signal processing methods that consider the temporal, spectral and dynamical features of this rhythm. We recorded action potentials (AP) from single cells in isolated fibrillating hearts with the aid of a floating microelectrode technique, and in cardiac tissue using optical fluorescence imaging. 1) Time-frequency analysis of these signals reveals different characteristics of VF signal during early and late stages of fibrillation. Perfusion of the heart maximized the short-term, high-frequency events, while without perfusion, the time-frequency distributions showed dispersion. Time-frequency analysis was thus shown to be helpful in characterizing AP during various stages of VF. 2) Parametric modeling was next considered to determine the evolution of fibrillation with extended periods of time, a situation that would arise during resuscitation procedures. Autoregressive modeling of VF signals was carried out to identify changes in the dominant poles of the VF signals with time course of evolution of VF. Parametric modeling of VF was thus shown to be useful in predicting the duration of cardiac arrest. 3) Finally, we sought to determine how dynamics of VF signals change with time, and in particular whether fibrillation can be considered chaotic at the cellular levels. Dynamical analysis, carried out by the methods of dimensional analysis and Lyapunov exponents revealed that VF has a relatively low dimensional attractor at the single cell level even though VF on the heart or the body surface may be a high dimensional process. Such analyses may help suggest methods to track and modify low dimensional chaotic VF in its early stages of evolution. 4) Finally, algorithms were developed for application in a clinical device such as the implantable cardioverter-defibrillator. Here, the emphasis is on the reducing false positive and false negative rates. This objective is accomplished using a sequential hypothesis testing algorithm that trades off accuracy for detection time and vice versa. In summary, non-parametric, parametric, and dynamical methods together provide quantitative insights into the fibrillation phenomenon at the cellular and whole heart levels, and may help in discrimination of various stages and forms of VF for the purposes of possible therapy.

Keywords

Ventricular Tachycardia Ventricular Fibrillation Correlation Dimension Normal Sinus Rhythm Fundamental Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Arnsdorf, M. F., 1990. The cellular basis of cardiac arrhythmias. A matrical perspective, Annals N. Y Acad. Sci., 601: 263–280.CrossRefGoogle Scholar
  2. 2.
    do Bakker, J. M. T., van Capelle, F. J. L., Janse, M. J., Wilde, A. A. M., Coronel, R., Becker, A. E., Dingemans, K. P., van Herne!, N. M., and R. N. W. Hauer, 1988. Reentry as a cause of ventricular tachycardia in patients with chronic ischemic heart disease: Electrophysiologic and anatomic correlation, Circulation 77: 589–606.CrossRefGoogle Scholar
  3. 3.
    Bernstein, R. C., and Frame. L. H., Ventricular reentry around a fixed barrier. Resetting with advancement in an ìn vitro model, Circulation 81: 267–280.Google Scholar
  4. 4.
    Panflov, A. V., and Keener, J. P.. 1993, Generation of reentry in anisotropic myocardium../ Cardiovasc. Electrophysiol. 4: 412–421.CrossRefGoogle Scholar
  5. 5.
    Moe. G. K., 1962. On the multiple wavelet hypothesis of atrial fibrillation, Archives Int. Pharmacodynamics 140: 183–188.Google Scholar
  6. 6.
    Kaplan, D. T., and Cohen, R. J., 1990, Is fibrillation chaos?, Cire. Res. 67: 886–892.CrossRefGoogle Scholar
  7. 7.
    Goldberger, A. L.. and Rigney, D. R., 1988, Sudden death is not chaos, In: Dynamic Patterns in Complex Systems, Kelso, J. A. S., Mandell, A. J., Shlusinger, M. F., (ed.), World Scientific Publ: Singapore, pp. 248–264.Google Scholar
  8. 8.
    Hodgson, D., 1991, Electrophysiology of ventricular fibrillation studied by the microelectrode method, M.S.E. Dissertation, Johns Hopkins University, Baltimore, MD.Google Scholar
  9. 9.
    Hodgson, D., Fishier, M. G., Chan, R., and Thakor, N. V., 1992, Intracellular recordings during VF: role of ion channels from experiments and comptuter models, Proc. Comput. Cardiol., IEEE Comput. Soc. Press, pp. 537–540.Google Scholar
  10. 10.
    Carlisle, E. J. F., Allen, J. D., Bailey, A., et. al., 1988, Fourier analysis of ventricular fibrillation and synchronization of DC countershocks in defibrillation, J. Electrocardiol., 21: 337.Google Scholar
  11. 11.
    Baykal, A., Ranjan, R., and Thakor, N. V., 1996, Estimation of ventricular fibrillation duration by autoregressive modeling, IEEE Trans. Biomed. Eng.,in press.Google Scholar
  12. 12.
    Ideker, R. E., Klein, G. J., Harrison, L., Smith. W. M., Kassel, J., Reimer, K. A., Wallace, A. G., and Gallagher, J. J., 1981. The transition of ventricular fibrillation induced by reperfusion following acute ischemia in the dog, Circulation 63: 1371–1379.Google Scholar
  13. 13.
    El-Sherif, N.. 1985, The Figure 8 model of reentrant excitation in the canine postinfarction model, In: Cardiac Electrophysiology and Arrhythmias, Zipes D.P., Jalife J, (eds.), Gnme Stratton: Orlando, pp. 363–378.Google Scholar
  14. 14.
    Allessie, M. A., Bonke, F. I. M., and Schopman, F. J. G., 1977, Interactions across an inexcitable region as a cause of ectopie activity in acute regional myocardial ischemia. A study in intact porcine and canine hearts and computer models, Circ. Res. 50: 527–537.Google Scholar
  15. 16.
    Dzwonczyk, R., Brown, C. G., and Werma, H. A., 1990, The median frequency of the ECG during ventricular fibrillation: Its use in an algorithm for estimating the duration of cardiac arrest, IEE Trans. Biomed. Eng. 37: 640–645.Google Scholar
  16. 17.
    Akiyama, T. 1981, Intracellular recording of in situ ventricular cells during ventricular fibrillation, Am. J. Physiol. 240: H465 - H471.Google Scholar
  17. 18.
    Allessie, M. A., Schatij, M. J., Kirchhof, C. J., Boersma, L., Huyberts, M.and Hollen, J., 1990, Electrophysiology of spiral waves in two dimensions: the role of anisotropy, Annals N.Y Acad. Sci. 591: 247–256.Google Scholar
  18. 19.
    Davidenko, J. M., Pertsov, A. V., Salomonsz, R., Baxter, W., and Jalife, J., 1992. Stationary and drifting spiral waves of excitation in isolated cardiac muscle, Nature 355: 349–351.CrossRefGoogle Scholar
  19. 20.
    Dillon, S., and Morad, M., 1981, A new laser scanning system for measuring action potential propagation in the heart, Science 214: 453–456.CrossRefGoogle Scholar
  20. 21.
    Efimov, I. R., Huang, D. T., and Salama, G., 1994, Optical mapping of repolarization and refractoriness from intact hearts, Circulation 90: 1469–1480.CrossRefGoogle Scholar
  21. 22.
    Knisley, S. B., Blitchington, T. F., Hill, B. C., Grant, A. O., Smith, W. M., Pilkington, T. C., and Ideker, R. E., 1993, Optical measurements of transmembrane potential changes during electric field stimulation of ventricular cells, Cire. Res. 72: 255–270.Google Scholar
  22. 23.
    Dzwonczyk, R., Brown, C. G., and Verma, H. A., 1990, The median frequency of the ECG during ventricular fibrillation: its use in an algorithm for estimating the duration of cardiac arrest, IEEE Trans. Biomed. Eng. 37: 640–646.Google Scholar
  23. 24.
    Camm, A. J., Davies, D. W., and Ward, D. E., 1987, Tachycardia recognition by implantable electronic devices, PACE Pacing Clin. Electrophysiol. 10: 1175–1190.Google Scholar
  24. 25.
    Geselowitz, D. B., Smith, S., Mowrey, K., and Berbari. E. J.. 1991, Model studies of extracellular electrograms arising from an excitation wave propagating in a thin layer. IEEE Trans. Biomed. Eng. 38: 526–531.CrossRefGoogle Scholar
  25. 26.
    Greenhut, S. E., Dicarlo, L. A., Jenkins, J. M., Throne, R. D. and Winston, S. A., 1991, Identification of ventricular tachycardia using intracardiac electrograms: a comparison of unipolar versus bipolar waveform analysis, PACE Pacing Clin. Electrophysiol. 14: 427–433.Google Scholar
  26. 27.
    Lin, D., DiCarlo, L. A. and Jenkins, J. M. 1988, Identification of ventricular tachycardia using intracavitary electrograms: analysis of time and frequency domain patterns, PACE 11: 41–249.CrossRefGoogle Scholar
  27. 28.
    Throne, R. D., Jenkins, J. M., and DiCarlo, L. A.. 1990. Intraventricular electrogram analysis for ventricular tachycardia detection: statistical validation, PACE Pacing Clin. Electrophysiol. 13: 15961601.Google Scholar
  28. 29.
    Chen, S., Thakor, N. V., and Mower, M. M., 1987, Ventricular fibrillation detection by a regression test on the autocorrelation function, Med. Biol. Eng. Comput. 25: 241–249.CrossRefGoogle Scholar
  29. 30.
    Steinhaus, B. M., Wells, R. T., Greenhut, S. E., Maas, S. M., Nappholz, T. A., Jenkins, J. M.. and DiCarlo, L. A., 1990, Detection of ventricular tachycardia using scanning correlation analysis, PACE. Pacing Clin. Electrophysiol. 13: 1930–1936.Google Scholar
  30. 31.
    Nygards, M., and Hutting, J., 1977, Recognition of ventricular fibrillation utilizing the power spectrum of the ECG, Proc. Comput. Cardiol., IEEE Comput. Soc. Press, pp. 393–397.Google Scholar
  31. 32.
    Babloyantz, A. and Destexhe, A., 1988, Is the normal heart a periodic oscillator? Biol. Cybernetics 58: 203–211.MathSciNetCrossRefGoogle Scholar
  32. 33.
    Casaleggio, A., Ranjan, R., and Thakor, N. V., 1994, Correlation dimension analysis of epicardial cell action potentials during different cardiac arrhythmias, Proc. Comput. Cardiol., IEEE Comput. Soc. Press, pp. 697–700.Google Scholar
  33. 34.
    Russel, D. C., Smith, H. J., and Oliver, M. F., 1979, Transmembrane potential changes and ventricular fibrillation during repetitive myocardial ischemia in the dog, Br. Heart J. 42: 88–96.CrossRefGoogle Scholar
  34. 35.
    Hogancamp, C. E., Kardech, M., Danforth, W. H., and Bing, R. J., 1959, Transmembrane electrical potentials in ventricular tachycardia and fibrillation, Am. Heart J. 57: 214–222.CrossRefGoogle Scholar
  35. 36.
    Joyner, R. W., Picone, J., Veenstra, R., and Rawling, D., 1983, Propagation through electrically coupled cells. Effects of regional changes in membrane properties, Circ. Res. 53: 526–534.CrossRefGoogle Scholar
  36. 37.
    Morkrid, L., Ohm, O. J., and Engedal, H., 1984, Time domain and spectral analysis of electrograms in man during regular ventricular activity and ventricular fibrillation, IEEE Trans. Biomed. Eng. 31: 350–355.CrossRefGoogle Scholar
  37. 38.
    Cohen, L., 1989, Time-frequency distributions: a review, Proc. IEEE 77: 941–981.CrossRefGoogle Scholar
  38. 39.
    Boashash, B., 1992, Time-Frequency Signal Analysis: Methods and Applications, Australia: Longman Cheshire.Google Scholar
  39. 40.
    Yi-Sheng, Z., Bin, Z., and Thakor, N. V., 1996, Variable convergence adaptive filter and its application to cardiac action potential, Med. Biol. Eng. Comput.,in press.Google Scholar
  40. 41.
    Thakor, N. V., and Yi-Sheng, Z., 1991, Some applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection, IEEE Trans. Biomed. Eng. 38: 785–794.CrossRefGoogle Scholar
  41. 42.
    Laguna, P., Jane, R., Meste, O., Poon, P., Caminal, P., Rix, H., and Thakor, N. V., 1992, Adaptive filter for event-related bioelectric signals using an impulse correlated reference input: comparison with signal averaging techniques, IEEE Trans. Biomed. Eng. 39: 1032–1044.CrossRefGoogle Scholar
  42. 43.
    Spach, M. S., Miller III, W. T., MillerPJones, E., Warren, R. B., 1979, Extracellular potentials related to intracellular action potentials during impulse conduction in anisotropic canine cardiac muscle. Circ. Res. 45: 188–204.Google Scholar
  43. 44.
    Baykal, A.. Ranjan, R., and Thakor, N. V., 1994, Model based analysis of ECG during early stages of ventricular fibrillation, J. Electrocardiol. 27 (suppl.): 84–90.Google Scholar
  44. 45.
    Kaplan, D., Smith, J., Saxberg, B., and Cohen, R., 1988, Nonlinear dynamics in cardiac conduction, Math. Biosci. 90: 19–48.MathSciNetMATHCrossRefGoogle Scholar
  45. 46.
    Parker,T. S., and Chua, L. O. 1987, Chaos: a tutorial for engineers, Proc. IEEE 75: 982–1008.CrossRefGoogle Scholar
  46. 47.
    Casaleggio, A., Ranjan, R., and Thakor, N. V., 1996, Dimensional analysis of the electrical activity at the epicardium of isolated hearts. Int. J. Bifurc. Chaos, to be published.Google Scholar
  47. 48.
    Jenkins. J., Bump, T., and Mukenbeck, F., 1984, Tachycardia detection in implantable antitachycardia devices, PACE Pacing Clin. Electrophysiol. 7: 1273–1277.CrossRefGoogle Scholar
  48. 49.
    Khoury, D. S., and Wilkoff, B. L., 1990, Tachycardia recognition algorithms for implantable systems, IEEE Eng. Med. Biol. Mag. 9: 40–42.CrossRefGoogle Scholar
  49. 50.
    Ropella, K. M., Baerman, J. M., Sahakian, A. V.. and Swiryn, S., 1990, Differentiation of ventricular tachyarrhythmias, Circulation 82: 2035–2043.CrossRefGoogle Scholar
  50. 51.
    Thakor, N. V., Zhu, Y. S., and Pan, K. Y., 1990, Ventricular tachycardia and fibrillation detection by a sequential hypothesis testing algorithm, IEEE Trans. Biomed. Eng. 37: 837–843.CrossRefGoogle Scholar
  51. 52.
    DiCarlo, L. A., Throne, R. D., and Jenkins, J. M., 1991, A time-domain analysis of intracardiac electrograms for arrhythmia detection, PACE Pacing Clin. Electrophysiol. 14: 329–336.CrossRefGoogle Scholar
  52. 53.
    Shoupu, C., Thakor, N. V., and Mower, M. M., 1987, Ventricular fibrillation detection by regression test of autocorrelation function, Med. Biol. Eng. Comput. 25: 241–249.CrossRefGoogle Scholar
  53. 54.
    Thakor, N. V. and Pan, K., 1990, Tachycardia and Fibrillation Detection by Automatic Implantable Cardioverter-Defibrillators: Sequential Testing in the Time Domain, IEEE Eng. Med. Biol. Soc. Mag. 9: 21–24.CrossRefGoogle Scholar
  54. 55.
    Thakor, N. V., Natarajan, A., and Tomaselli, G., 1994, Multiway sequential hypothesis testing for tachyarrhythmia discrimination, IEEE Trans. Biomed. Eng. 41: 480–487.CrossRefGoogle Scholar
  55. 56.
    Fishier, M. G. and Thakor, N. V., 1991, massively parallel computer model of propagation through two dimensional cardiac syncytium, PACE Pacing Clin. Electrophysiol. 14 (Part II ): 1694–1699.Google Scholar
  56. 57.
    Mirowski, M.. and Mower. M. M., 1985, The automatic implantable defibrillator, Am. Heart J. 100: 996–1004.Google Scholar
  57. 58.
    Thakor, N. V., 1984, From Holter monitors to automatic defibrillators: developments in ambulatory arrhythmia monitoring, IEEE Trans. Biomed. Eng. 31: 700–708.Google Scholar
  58. 59.
    Fisher, J. D., Kim, S. G., and Mercando, A. D., 1988, Electrical devices for treatment of arrhythmias, Am. J. Cardiol. 61: 45a - 57a.CrossRefGoogle Scholar
  59. 60.
    Casaleggio, A., 1993, Differences on the correlation dimension of MIT-BIH ECG database recordings, Proc. Comput. Cardiol., IEEE Comput. Soc. Press, pp. 539–542.Google Scholar
  60. 62.
    Ropella, K. M., A. V., M., Swiryn, S., 1989, The coherence spectrum. A quantitative discriminator of fibrillatory and nonfibrillatory cardiac rhythms, Circulation 80: 112–119.Google Scholar

Copyright information

© Springer Science+Business Media New York 1996

Authors and Affiliations

  • Nitish V. Thakor
    • 1
  • Ahmet Baykal
    • 1
  • Aldo Casaleggio
    • 2
  1. 1.Biomedical Engineering DepartmantThe Johns Hopkins UniversityBaltimoreUSA
  2. 2.ICE-CNEGenovaItaly

Personalised recommendations