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Application of chaos theory to biology and medicine

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Abstract

The application of “chaos theory” to the physical and chemical sciences has resolved some long-standing problems, such as how to calculate a turbulent event in fluid dynamics or how to quantify the pathway of a molecule during Brownian motion. Biology and medicine also have unresolved problems, such as how to predict the occurrence of lethal arrhythmias or epileptic seizures. The quantification of a chaotic system, such as the nervous system, can occur by calculating the correlation dimension (D2) of a sample of the data that the system generates. For biological systems, the point correlation dimension (PD2) has an advantage in that it does not presume stationarity of the data, as the D2 algorithm must, and thus can track the transient non-stationarities that occur when the systems changes state. Such non-stationarities arise during normal functioning (e.g., during an event-related potential) or in pathology (e.g., in epilepsy or cardiac arrhythmogenesis). When stochastic analyses, such as the standard deviation or power spectrum, are performed on the same data they often have a reduced sensitivity and specifity compared to the dimensional measures. For example, a reduced standard deviation of heartbeat intervals can predict increased mortality in a group of cardiac subjects, each of which has a reduced standard deviation, but it cannot specify which individuals will or will not manifest lethal arrhythmogenesis; in contrast, the PD2 of the very same data can specify which patients will manifest sudden death. The explanation for the greater sensitivity and specificity of the dimensional measures is that they aredeterministic, and thus are moreaccurate in quantifying the time-series. This accuracy appears to be significant in detecting pathology in biological systems, and thus the use of deterministic measures may lead to breakthroughs in the diagnosis and treatment of some medical disorders.

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References

  • Albano, A.M., Abraham, N.B., Guzman de, G.C., Tarropja, M.F.H., Bandy, D.K., Gioggia, R.S., Rapp, P.E., Zimmerman, I.D., Greenbaun, N.N., & Bashore, T.R. (1986). Lasers and brains: Complex systems with low-dimensional attractors. In G. Mayer-Kress (Ed.),Dimensions and entropies in chaotic systems (pp. 231–240). Berlin: Springer.

    Google Scholar 

  • Babloyantz, A. (1985). Strange attractors in the dynamics of brain activity. In H. Haken (Ed.),Complex systems—Operational approaches in neurobiology, physics, and computers (pp. 116–122). Berlin: Springer.

    Google Scholar 

  • Babloyantz, A. (1986). Evidence of chaotic dynamics of brain activity during the sleep cycle. In G. Mayer-Kress (Ed.),Dimensions and entropies in chaotic systems (pp. 241–245). Berlin: Springer.

    Google Scholar 

  • Babloyantz, A., & Destexhe, A. (1986). Low-dimensional chaos in an instance of epilepsy.Proceedings of the National Academy of Sciences, USA,83, 3513–3517.

    Article  Google Scholar 

  • Babloyantz, A. (1988). Is the normal heart a periodic oscillator?Biological Cybernetics, 58, 203–211.

    Article  PubMed  Google Scholar 

  • Babloyantz, A. (1990). Chaotic dynamics in brain activity. In E. Basar, (Ed.),Chaos in brain function (pp. 42–48.). Berlin: Springer.

    Google Scholar 

  • Basar, E. (1990).Chaos in brain function. New York: Springer-Verlag.

    Google Scholar 

  • Bigger, J.T., Kleiger, R.E., Fleiss, J.L., Rolnitzky, L.M., Steinman, R.C., & Miller, J.P. (1988). Multicenter post-infarction research group: Components of heart rate variability measured during healing of acute myocardial infarction.American Journal of Cardiology, 61, 208–215.

    Article  PubMed  Google Scholar 

  • Bigger, J.T. Jr., La Rovere, M.T., Steinman, R.C., Fleiss, J.L., Rottman, J.N., Rolnitzky, L.M., & Schwartz, P.J. (1989). Comparison of baroreflex sensitivity and heart period variability after myocardial infarction.Journal of the American College of Cardiology, 14, 1511–1518.

    Article  PubMed  Google Scholar 

  • Billman G.E., Schwartz P.J., & Stone H.L. (1982). Baroreceptor reflex control of heart rate: A predictor of sudden cardiac death.Circulation, 66, 874–880.

    Article  PubMed  Google Scholar 

  • Cacace, A.T., Satya-Murti, S., & Wolpaw, J.R. (1990). Human middle-latency auditory evoked potentials: Vertex and temporal components.Electroencephalography and Clinical Neurophysiology, 77, 6–18.

    Article  PubMed  Google Scholar 

  • Chialvo D.R., & Jalife J. (1987). Non-linear dynamics of cardiac excitation and impulse propagation.Nature, 330, 749–752.

    Article  PubMed  Google Scholar 

  • Desmedt, J.E., & Tomberg, C. (1989). Mapping early somatosensory evoked potentials in selective attention: Critical evaluation of control conditions used for titrating by difference the cognitive P30, P40, P100 and N140.Electroencephalography and Clinical Neurophysiology, 74, 321–346.

    Article  PubMed  Google Scholar 

  • Ditto, W.L., Rauseo, S.N., & Spano, M.L. (1990). Experimental control of chaos.Physical Review Letters, 65, 3211–3214.

    Article  PubMed  Google Scholar 

  • Donchin, E., Karis, D., Bashore, T.R., Coles, M.G.H., & Gratton, G. Cognitive psychophysiology and human information processing (1986). In M.G.H. Coles, E. Donchin, & S. Porges (Eds.),Psychophysiology: Systems, processes and applications. (pp. 244–267). New York: Guildford Press.

    Google Scholar 

  • Donchin, E., Ritter, W., & McCallum, W.C. (1978). Cognitive psychophysiology: The endogenous components of the ERP. In E. Callaway, P. Tueting, & S.H. Koslow, (Eds.),Event-related potentials in man. (pp. 349–412). New York: Academic Press.

    Google Scholar 

  • Elbert, T., & Rockstroh, B. (1987). Threshold regulation—A key to the understanding of the combined dynamics of EEG and event-related potentials.Journal of Psychophysiology, 4, 317–333.

    Google Scholar 

  • Farmer, J.D., Ott, E., & Yorke, J.A. (1983). Dimension of chaotic attractors.Physica 7D, 153–180.

    Google Scholar 

  • Freeman, W., & Skarda, C.A. (1985). Spatial EEG-patterns, non-linear dynamics and perception: The neo-Sherringtonian view.Brain Research Reviews, 10, 147–175.

    Article  Google Scholar 

  • Gillis, R.A., Corr, P.B., Pace, D.G., Evans, D.E., DiMicco, J., & Pearle, D.L. (1976). Role of the nervous system in experimentally induced arrhythmias.Cardiology, 61, 37–49.

    Article  PubMed  Google Scholar 

  • Gleick, J. (1987)Chaos: Making a new science. New York: Penguin.

    Google Scholar 

  • Goldberger, A.L., Rigney, D.R., Mietus, J., Antman, E.M., & Greenwald, S. (1988). Nonlinear dynamics in sudden cardiac death syndrome: Heartrate oscillations and bifurcations.Experientia, 44, 983–987.

    Article  PubMed  Google Scholar 

  • Graf, K.E., & Elbert, T. (1990). Dimensional analysis of the waking EEG. In E. Basar (Ed.),Chaos in brain function. (pp. 135–152). Berlin: Springer.

    Google Scholar 

  • Grassberger, P., & Procaccia, I. (1983). Measuring the strangeness of strange attractors.Physica, 9D, 183–208.

    Google Scholar 

  • Gray, C.M., Konig, P., Engel, A.K., & Singer, W. (1989). Oscillatory response in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties.Nature, 338, 334–337.

    Article  PubMed  Google Scholar 

  • Gray, C.M., & Singer, W. (1989). Stimulus specific neuronal oscillations in orientation columns of cat visual cortex.Proceedings of the National Academy of Sciences, USA,86, 1968–1702.

    Article  Google Scholar 

  • Guevara, M.R., Glass, L., & Shrier. A. (1981). Phase locking, period-doubling bifurcations, and irregular dynamics in periodically stimulated cardiac cells.Science, 214, 1350–1353.

    Article  PubMed  Google Scholar 

  • Haken, H. (1983).Advanced synergetics. New York: Springer-Verlag.

    Google Scholar 

  • Hull, S.S., Evans, A.R., Vanoli, E., Adamson, P.B., Stramba-Badiale, M., Albert, D.E., Foreman R.D., & Schwartz P.J. (1990). Heart rate variability before and after myocardial infarction in conscious dogs at high and low risk of sudden death.Journal of the American College of Cardiology, 16, 978–985.

    Article  PubMed  Google Scholar 

  • Karmos, G., Molnar, M., & Csepe, V. (1986). Intracortical profiles of evoked potential components related to behavioural activation in cats. In W.C. McCallum, R. Zappoli, & F. Denoth (Eds.),Cerebral psychophysiology: Studies in event-related potentials (pp. 555–557). EEG Suppl. 38. Amsterdam: Elsevier Science Publishers B.V.

    Google Scholar 

  • Kleiger, R.E., Miller, J.P., Bigger, J.T., & Moss, A.J. (1987). Multicenter post-infarction research group: Decreased heart rate variability and is association with increased mortality after acute myocardial infarction. American Journal of Cardiology,59, 256–262.

    Article  PubMed  Google Scholar 

  • Kleiger, R.E., Miller, J.P., Krone R.J., & Bigger, J.T. (1990). Multicenter postinfarction research group: The independence of cycle length variability and exercise testing on predicting mortality of patients surviving acute myocardial infarction.American Journal of Cardiology, 65, 408–411.

    Article  PubMed  Google Scholar 

  • La Rovere, M.T., Specchia, G., Mortara, A., & Schwartz, P.J. (1988). Baroreflex sensitivity, clinical correlates and cardiovascular mortality among patients with a first myocardial infarction: A prospective study.Circulation, 78, 816–824.

    Article  PubMed  Google Scholar 

  • Lombardi, F., Sandorne, G., Pernpruner, S., Sala, R., Garimoldi, M., Cerutti, S., Baselli, G., Pagani, M., & Malliani, A. (1987). Heart rate variability as an index of sympathovagal interaction after acute myocardial infarction. American Journal of Cardiology,60, P1239–1245.

    Article  Google Scholar 

  • Mandelbrot, B.B. (1983).The fractal geometry of nature. New York: Freeman and Co.

    Google Scholar 

  • Martin, G.J., Magid, N.M., Myers, G., Barnett, P.S., Schaad, J.W., Weiss J.S., Lesch, M., & Singer, D.H. (1987). Heart rate variability and sudden death secondary to coronary artery disease during ambulatory electrocardiographic monitoring.American Journal of Cardiology, 60, 86–89.

    Article  PubMed  Google Scholar 

  • Mayer-Kress, G., Yates, F.E., Benton, L., Keidel, M., Tirsch, W., Poppl, S.J., & Geist, K. (1988). Dimensional analysis of non-linear oscillations in brain, heart and muscle.Mathematical Biosciences, 90, 155–182.

    Article  Google Scholar 

  • Mitra, M., & Skinner, J.E. (in press). Low-dimensional chaos in the olfactory bulb of the conscious rabbit: A novel odor evokes spatially-uniform increases in the correlation dimensions of surface potentials.Behavioral Neuroscience.

  • Molnar, M., Karmos, G., Csepe, V., & Winkler, I. (1988). Intracortical auditory evoked potentials during classical aversive conditioning in cats.Biological Psychology, 26, 339–350.

    Article  PubMed  Google Scholar 

  • Molnar, M., & Skinner J. (in press). Low-dimensional chaos in event-related potentials.International Journal of Neuroscience.

  • Multicenter Postinfarction Research Group. (1983). Risk stratification and survival after myocardial infarction.New England Journal of Medicine, 309, 31–336.

    Google Scholar 

  • Myers, G.A., Martin, G.J., Magid, N.M., Barnett, P.S., Schaad, J.W., Weiss, J.S., Lesch, M., & Singer, D.H. (1986). Power spectral analysis of heart rate variability in sudden cardiac death: Comparison to other methods.IEEE Transactions on Biomed Eng, BME-33,12, 1149–1157.

    Article  Google Scholar 

  • Naatanen, R. (1987). Event-related potentials in research of cognitive processes—A classification of components. In E. van der Meer, J. Hoffmann (Eds.),Knowledge aided information processing (pp. 241–273). Amsterdam: Elsevier.

    Google Scholar 

  • Ott, E., Grebogi, C., & Yorke, J.A. (1990). Controlling chaos. In D.K. Campbell (Ed.),Chaos (pp. 153–172). New York: American Institute of Physics.

    Google Scholar 

  • Packard, N.H., Crutchfield, J.P., Farmer, J.D., & Shaw, R.S. (1980) Geometry from a time series.Physical Review Letters, 45, 712–716.

    Article  Google Scholar 

  • Parker, G.W., Michael, L.H., Hartley, C.J., Skinner, J.E., & Entman, M.L. (1990). Central beta-adrenergic mechanisms may modulate ischemic ventricular fibrillation in pigs.Circulation Research, 66, 259–279.

    PubMed  Google Scholar 

  • Pratt, C.M., Theroux, P., Slymen, D., Riordan-Bennett, A., Morisette, D., Galloway, A., Seals, A.A., & Holstrom, A. (1987). Spontaneous variability of ventricular arrhythmias in patients at increased risk for sudden death after acute myocardial infarction: Consecutive ambulatory electrocardiographic recordings in 88 patients.American Journal of Cardiology, 59, 278–283.

    Article  PubMed  Google Scholar 

  • Rapaport, E. (1988). Sudden cardiac death.American Journal of Cardiology, 62, 3I-6I.

    Article  PubMed  Google Scholar 

  • Rapp, P.E., Bashore, T.R., Martineire, J.M., Albano, A.M., Zimmerman, I.D., & Mees, A.I. (1989). Dynamics of brain electrical activity.Brain Topography, 2, 99–118.

    Article  PubMed  Google Scholar 

  • Rapp, P.E., Bashore, T.R., Zimmerman, I.D., Martinerie, J.M., Albano, A.M., & Mees, A.I. (1990). Dynamical characterization of brain electrical activity. In S. Krasner (Ed.),The ubiquity of chaos (pp. 10–22). Washington, DC: American Association for the Advancement of Sciences.

    Google Scholar 

  • Rich, M.A.W., Saini, J.S., Kleiger, R.E., Carney, R.M., teVelde, A., & Freedland, K.E. (1988). Correlation of heart rate variability with clinical and angiographic variables and late mortality after coronary angiography.American Journal of Cardiology, 62, 714–717.

    Article  PubMed  Google Scholar 

  • Roschke, J., & Basar, E. (1990). The EEG is not a simple noise: Strange attractors in intracranial structures. In E. Basar (Ed.),Chaos in brain function (pp. 49–62). New York: Springer-Verlag.

    Google Scholar 

  • Schuster, H.G. (1988).Deterministic chaos. VCH: Weinheim.

    Google Scholar 

  • Skinner, J.E., Carpeggiani, C., Landisman, C.E., & Fulton, K.W. (1991a). The correlation-dimension of the heartbeat is reduced by myocardial ischemia in conscious pigs.Circulation Research, 68, 966–976.

    PubMed  Google Scholar 

  • Skinner, J.E., Goldberger, A.L., Mayer-Kress, G., & Ideker, R.E. (1990a). Chaos in the heart: Implications for clinical cardiology.Biotechnology, 8, 1018–1024.

    Article  Google Scholar 

  • Skinner, J.E., Lie, J.T., & Entman, M.L. (1975). Modification of ventricular fibrillation latency following coronary artery occlusion in the conscious pig: The effects of psychological stress and beta-adrenergic blockade.Circulation, 51, 656–667.

    PubMed  Google Scholar 

  • Skinner, J.E., Martin, J.L., Landisman, C.E., Mommer, M.M., Fulton, K., Mitra, M., Burton, W.D., & Saltzberg, B. (1990b). Chaotic attractors in a model of neocortex: Dimensionalities of olfactory bulb surface potentials are spatially uniform and event related. In E. Basar (Ed.),Chaos in brain function (pp. 119–134). New York: Springer-Verlag.

    Google Scholar 

  • Skinner, J.E., Mitra, M., & Fulton, K. (1991b). Low-dimensional chaos in a simple biological model of neocortex: Implications for cardiovascular and cognitive disorders. In J.G. Carlson, & A.R. Seifert (Eds.),An international perspective on self-regulation and health (pp. 95–117). New York: Plenum.

    Google Scholar 

  • Skinner, J.E., Molnar, M., & Harper, R.M. (in press). Higher cerebral regulation of cardiovascular and respiratory function. In M.H. Kryger, T. Roth, & W.C. Dement (Eds.),Principles and practice of sleep medicine (2nd ed). Philadelphia: W.B. Saunders Co.

  • Skinner, J.E., Pratt, C.M., & Vybiral, T. (in press). Low-dimensional chaos in heartbeat intervals predicts sudden arrhythmic death in cardiac patients.Circulation Research.

  • Skinner, J.E., & Reed, J.C. (1981). Blockade of a frontocortical-brainstem pathway prevents ventricular fibrillation of the ischemic heart in pigs.American Journal of Physiology, 240, H156-H163.

    PubMed  Google Scholar 

  • Skinner, J.E., & Yingling, C.D. (1977). Central gating mechanisms that regulate event-related potentials and behavior: A neural model for attention. In J.E. Desmedt (Ed.),Progress in Clinical Neurophysiology, Vol. I (pp. 30–69). Brussels: Karger-Basel.

    Google Scholar 

  • Takens, F. (1981). Detecting strange attractors in turbulance.Lecture Notes in Mathematics, 898, 366–381.

    Article  Google Scholar 

  • Takens, F. (1985). On the numerical determination of the dimension of an attractor.Lecture Notes in Mathematics, 1125, 99–106.

    Article  Google Scholar 

  • Theiler, J. (1988). Quantifying chaos: Practical estimation of the correlation dimension. Unpublished thesis. California Institute of Technology, Pasadena, California.

  • Verrier, R.L., & Lown, B. (1981). Autonomic nervous system and malignant cardiac arrhythmias. In H. Weiner, M.A. Hofer, A.J. Stunkard (Eds.),Brain, behavior, and bodily disease (pp. 273–291). New York: Raven.

    Google Scholar 

  • Vaughan, H.G., & Arezzo, J.C. (1988). The neural basis of event-related potentials. In T.W. Picton, (Ed.),Human event-related potentials. (pp. 45–96). EEG Handbook Revised Series, Vol. 3. Amsterdam: Elsevier Science Publishers B.V.

    Google Scholar 

  • Wilson, D.A., & Leon, M. (1988). Spatial patterns of olfactory bulb single-unit responses to learned olfactory cues in young rats.Journal of Neurophysiology, 59, 1770–1782.

    PubMed  Google Scholar 

  • Winfree, A.T. (1987).When time breaks down: The three-dimensional dynamics of electrochemical waves and cardiac arrhythmias. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Wood, C.C., McCarthy, G., Squires, N.K., Vaughan, H.G., Woods, D.L., & McCallum, W.C. (1984). Anatomical and physiological substrates of event-related potentials. In R. Karrer, & P. Tueting (Eds.),Brain and information: Event-related potentials (pp. 681–721). Annals of the New York Academy of Sciences, Vol. 425. New York: New York Academy of Sciences.

    Google Scholar 

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Grant Support: National Institutes of Health HL31164 and NS27745

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Skinner, J.E., Molnar, M., Vybiral, T. et al. Application of chaos theory to biology and medicine. Integrative Physiological and Behavioral Science 27, 39–53 (1992). https://doi.org/10.1007/BF02691091

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