An expanded and updated version of this material appears in:
Bolis CL, Licinio J, eds. The Autonomic Nervous System. Geneva: World Health Organization, 1999.Please cite this publication when referencing this material, and also include the standard citation for PhysioNet:
Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23):e215-e220 [Circulation Electronic Pages; http://circ.ahajournals.org/cgi/content/full/101/23/e215]; 2000 (June 13)
Clinicians and basic investigators are increasingly aware of the remarkable upsurge of interest in nonlinear dynamics, the branch of the sciences widely referred to as "chaos theory." Those attempting to evaluate the biomedical relevance of this field confront a daunting array of terms and concepts, such as nonlinearity, fractals, periodic oscillations, bifurcations and complexity, as well as chaos (1-4). Therefore, the present discussion provides an introduction to some key aspects of nonlinear dynamics, with a particular emphasis on heart rate control. A major challenge is in making these concepts accessible, not only to basic and clinical investigators, but to medical and graduate students at a formative stage of their training.
To appreciate the general clinical relevance of dynamics to the heartbeat, consider the following common problem. What is the best way to compare a sequence of measurements obtained from two subjects, or from one individual or experimental procedure under different conditions? Conventionally, clinicians and investigators rely primarily on a comparison of means using appropriate statistical tests. However, the limitations of such traditional analyses become apparent when evaluating the data in Fig. 1, showing sinus rhythm heart rate plots collected from a healthy subject and one with congestive heart failure. Recording the instantaneous signal from any system over a continuous observation period generates a time series. What is noteworthy in this example is that these two time series have nearly identical means and variances, suggesting no clinically relevant differences. Yet, visual inspection indicates that the two sequences of data display a markedly different organization. The healthy heartbeat trace shows a complex, "noisy" type of variability, whereas the data set from the patient with heart failure reveals periodic oscillations in heart rate repeating about 1 cycle/minute (~.02 Hz). Time series analysis is concerned with quantifying the order of data points; nonlinear dynamics provides a deeper understanding of the mechanisms of patterns and differences such as those in Fig. 1.
In linear systems, the magnitude of the output (y) is controlled by that of the input (x) according to simple equations in the familiar form y=mx+b. A well-known example of such a relationship is Ohm's law: V=IR where the voltage (V) in a circuit will vary linearly with current (I), provided the resistance (R) is held constant. Two central features of linear systems are proportionality and superposition. Proportionality means that the output bears a straightline relationship to the input. Superposition refers to the fact that the behavior of linear systems composed of multiple components can be fully understood and predicted by dissecting out these components and figuring out their individual input-output relationships. The overall output will simply be a summation of these constituent parts. The components of a linear system literally "add up" - there are no surprises or anomalous behaviors.
In contrast, even simple nonlinear systems violate the principles of
proportionality and superposition. An example of a deceptively complex
nonlinear equation is
A related and noteworthy property of nonlinear dynamics is referred to as universality (1, 4). Surprisingly, nonlinear systems that appear to be very different in their specific details may exhibit certain common patterns of response. For instance, nonlinear systems may change in a sudden, discontinuous fashion. One important and universal class of abrupt, nonlinear transitions is called a bifurcation (1, 9). This term describes situations where a very small increase or decrease in the value of some parameter controlling the system causes it to change abruptly from one type of behavior to another. For example, the output of the same system may suddenly go from being wildly irregular to a highly periodic, or vice versa. A universal class of bifurcations occurring in a wide variety of nonlinear systems is the sudden appearance of regular oscillations that alternate between two values (15). This type of dynamic may underlie a variety of alternans patterns in cardiovascular dysfunction. A familiar example is the beat-to-beat alternation in QRS axis and amplitude seen in some cases of cardiac tamponade (16). This kind of electrical alternans is related to the back and forth swinging motion of the heart within the pericardial effusion. Multiple other examples of alternans in perturbed cardiac physiology have been described, including ST-T alternans which may precede ventricular fibrillation (17), and pulsus alternans during heart failure.
Although the focus of much recent attention, chaos per se actually comprises only one specific subtype of nonlinear dynamics. Prior to the work of the renowned French mathematician, Henri Poincaré, in the early 1900s, science was dominated by the seemingly inviolable tenet that the behavior of systems for which one could write out explicit equations (e.g., the solar system) should be, in principle, fully predictable for all future times (18). What Poincaré discovered (and what was more recently rediscovered) is that a complex type of variability can arise from the operation of even the simplest nonlinear system, such as that governed by the logistic equation mentioned earlier. Because the equations of motion which generate such erratic, and apparently unpredictable behavior do not contain any random terms, this mechanism is now referred to as deterministic chaos (1, 4). The colloquial use of the term chaos - to describe unfettered randomness, usually with catastrophic implications - is quite different from this specialized usage.
The extent to which chaos relates to physiological dynamics is a subject of active investigation and some controversy. At first it was widely assumed that chaotic fluctuations were produced by pathological systems such as cardiac electrical activity during atrial or ventricular fibrillation (19). However, this initial presumption has been challenged (20) and the weight of current evidence does not support the view that the irregular ventricular response in atrial fibrillation or that ventricular fibrillation itself represents deterministic cardiac chaos (21). Further, there is no convincing evidence that other arrhythmias sometimes labeled "chaotic," such as multifocal atrial tachycardia, meet the technical criteria for nonlinear chaos. An alternative hypothesis (22) is that the subtle but complex heart rate fluctuations observed during normal sinus rhythm in healthy subjects, even at rest, are due in part to deterministic chaos, and that a variety of pathologies, such as congestive heart failure syndromes, may involve a paradoxical decrease in this type of nonlinear variability (Fig. 1). Because the mathematical algorithms designed for detecting chaos are not reliably applied to nonstationary, relatively short and often noisy data sets obtained from most clinical and physiological studies, the intriguing question of the role, if any, of chaos in physiology or pathology remains unresolved (22-28).
D. Fractal Anatomy
The term fractal is a geometric concept related to, but not synonymous with chaos (29, 30). Classical geometric forms are smooth and regular and have integer dimensions (1,2, and 3, for line, surface, and volume respectively). In contrast, fractals are highly irregular and have non-integer, or fractional, dimensions. Consider a fractal line. Unlike a smooth Euclidean line, a fractal line, which has a dimension between 1 and 2, is wrinkly and irregular. Examination of these wrinkles with the low-power lens of a microscope, reveals smaller wrinkles on the larger ones. Further magnification shows yet smaller wrinkles, and so on. A fractal, then, is an object composed of subunits (and sub-subunits) that resemble the larger scale structure, a property known as self-similarity (Fig. 3). A wide variety of natural shapes share this internal look-alike property, including branching trees and coral formations, wrinkly coastlines, and ragged mountain ranges. A number of cardiopulmonary structures also have a fractal-like appearance (2, 3, 30, 31). Examples of self-similar anatomies include the arterial and venous trees, the branching of certain cardiac muscle bundles, as well as the ramifying tracheobronchial tree and His-Purkinje network.
From a mechanistic viewpoint, these self-similar cardiopulmonary structures all serve a common physiologic function: rapid and efficient transport over a complex, spatially distributed system. In the case of the ventricular electrical conduction system, the quantity transported is the electrical stimulus regulating the timing of cardiac contraction (31). For the vasculature, fractal branchings provide a rich, redundant network for distribution of O2 and nutrients and for the collection of CO2 and other metabolic waste products. The fractal tracheo-bronchial tree provides an enormous surface area for exchange of gases at the vascular-alveolar interface, coupling pulmonary and cardiac function (30). Fractal geometry also underlies other important aspects of cardiac function. Peskin and McQueen (32) have elegantly shown how fractal organization of connective tissue in the aortic valve leaflets relates to the efficient distribution of mechanical forces. A variety of other organ systems contain fractal structures that serve functions related to information distribution (nervous system), nutrient absorption (bowel), as well collection and transport (biliary duct system, renal calyces) (2, 3, 30).
An important extension of the fractal concept was the recognition that it applies not just to irregular geometric or anatomic forms that lack a characteristic (single) scale of length, but also to complex processes that lack a single scale of time (29, 33). Fractal processes generate irregular fluctuations on multiple time scales, analogous to fractal objects that have wrinkly structure on different length scales. Furthermore, such temporal variability is statistically self-similar. A crude, qualitative appreciation for the self-similar nature of fractal processes can be obtained by plotting the time series in question at different "magnifications," i.e., different temporal resolutions. For example, Fig. 3 plots the time series of heart rate from a healthy subject on three different scales. All three graphs have an irregular ("wrinkly") appearance, reminiscent of a coastline or mountain range. The irregularity seen on different scales is not visually distinguishable, an observation confirmed by statistical analysis (34, 35). The roughness of these time series, therefore, possesses a self-similar (scale-invariant) property.
Since scale-invariance appears to be is a general mechanism underlying many physiological structures and functions, one can adapt new quantitative tools derived from fractal mathematics for measuring healthy variability. Complex fluctuations with the statistical properties of fractals have not only been described for heart rate variability, but also for fluctuations in respiration (36), systemic blood pressure (37), human gait (38) and white blood cell counts (39), as well as certain ion channel kinetics (3). Furthermore, if scale-invariance is a central organizing principle of physiological structure and function, we can make a general, but potentially useful prediction about what might happen when these systems are severely perturbed. If a functional system is self-organized in such a way that it does not have a characteristic scale of length or time, a reasonable anticipation would be a breakdown of scale-free structure or dynamics with pathology (35). How does a system behave after such a pathologic transformation? The antithesis of a scale-free (fractal) system (i.e., one with multiple scales) is one that is dominated by a single frequency or scale. A system that has only one dominant scale becomes especially easy to recognize and characterize because such a system is by definition periodic - it repeats its behavior in a highly predictable (regular) pattern (Fig. 4). The theory underlying this prediction may account for a clinical paradox: namely, that a wide range of illnesses are associated with markedly periodic (regular) behavior even though the disease states themselves are commonly termed "dis-orders" (39).
The appearance of highly periodic dynamics in many disease states is one of the most compelling examples of the notion of complexity loss in disease (40). Complexity here refers specifically to a multiscale, fractal-type of variability in structure or function. Many disease states are marked by less complex dynamics than those observed under healthy conditions. This de-complexification of systems with disease appears to be a common feature of many pathologies, as well as of aging (40). When physiologic systems become less complex, their information content is degraded (41). As a result, they are less adaptable and less able to cope with the exigencies of a constantly changing environment. To generate information, a system must be capable of behaving in an unpredictable fashion (2, 42). In contrast, a highly predictable, regular output (i.e., a sine wave) is information-poor since it monotonously repeats its activity. (The most extreme example of complexity loss would be the total absence of variability - a straightline output.)
Quantitative assessment of periodic oscillations can be obtained by analyzing the time series of interest with a variety of standard mathematical tools. For systems producing a highly periodic output, the most widely used methods are based on spectral analysis. Remarkably, the time series of many severely pathologic systems have a nearly sinusoidal appearance; the spectrum shows a dominant peak at this characteristic frequency. An example is the heart rate variability sometimes observed in patients with severe congestive heart failure (Fig. 1) (43, 44) or with fetal distress syndromes (45). In contrast, systems with a fractal output (such as normal heart rate variability) show a type of broadband spectrum which includes many different frequencies (scales).
Probably the first explicit description of the concept of periodic diseases was provided over 30 years ago by Dr. Hobart Reimann (46). He called attention to a number of conditions in which the disease process itself could be shown to flare or recur on a regular basis of days to months; ranging from certain forms of arthritis to some mental illnesses and hereditary diseases, such as familial Mediterranean fever. In the late 1970s, Michael Mackey and Leon Glass (47, 48) helped to rekindle interest in this dormant field when they introduced the term dynamical disease to encompass the types of periodic syndrome Reimann had catalogued, as well as irregular dynamics thought possibly to represent deterministic chaos.
Reimann's original list was premised on the assumption that periodic conditions were somewhat unique, and even idiosyncratic, in clinical medicine. However, to the extent that healthy function is often characterized by a multi-scale fractal complexity, we would anticipate that the emergence of single-scale (i.e., non-fractal) states might be considerably more common, if not ubiquitous, in pathophysiology. Indeed, a recent survey of the literature (49) indicates that Reimann, rather than compiling a list of the exceptional, was more likely sampling a widespread, even generic manifestation of the dynamics of disease. From the most general perspective, the practice of bedside diagnosis itself would be impossible without the loss of complexity and the emergence of pathologic periodicities. To a large extent, it is these periodicities and highly-structured patterns - the breakdown of multi-scale fractal complexity under pathologic conditions - that allow clinicians to identify and classify many pathologic features of their patients. Familiar examples include periodic tremors in neurologic conditions, AV Wenckebach patterns, the "sine-wave" ECG pattern in hyperkalemia, manic-depressive alterations, and cyclic breathing patterns in heart failure.
While fractals are irregular, not all irregular structures or erratic time series are fractal. A key feature of the class of fractals seen in biology is a distinctive type of long-range order. This property generates correlations that extend over many scales of space or time. For complex processes, fractal long-range correlations are the mechanism underlying a "memory" effect; the value of some variable, e.g., heart rate at a particular time, is related not just to immediately preceding values, but to fluctuations in the remote past. Certain pathologies are marked by a breakdown of this long-range organization property, producing an uncorrelated randomness similar to "white noise." An example is the erratic ventricular response in atrial fibrillation over relatively short time scales. Peng et al. (50) have recently described a simple algorithm for quantifying the breakdown of long-range (fractal) correlations in physiological time series.
Practical applications of nonlinear dynamics are likely within the next few years. Probably the first bedside implementations will be in physiological monitoring. A number of indices derived from chaos theory have shown promise in forecasting subjects at high risk of electrophysiologic or hemodynamic instability, including
In addition to these diagnostic applications, perhaps the most exciting prospects are related to novel therapeutic interventions. An important recent finding is that certain mathematical or physical systems with complex dynamics can be controlled by properly timed external stimuli: chaotic dynamics can be made more regular (chaos control) and periodic ones can be made chaotic (chaos anti-control) (61-63). One proposal, based on the earlier notion that certain arrhythmias, particularly ventricular fibrillation, represent cardiac chaos, is to develop chaos control algorithms to electrically pace the heart beat back to sinus rhythm (63). A more recent proposal is to use chaos anti-control protocols to treat or to prevent cardiac arrhythmias or epilepsy based on the hypothesis that restoration of a kind chaotic-like variability may actually be advantageous (62).
Chaos theory also holds promise for illuminating a number of major problems in contemporary physiology and molecular biology. Nonlinear wave mechanisms may underlie certain types of reentrant ventricular tachyarrhythmias (6, 7). Appreciation for the rich nonlinearity of physiological systems may have relevance for modeling enormously complicated signal-transduction cascades involved, for example, in neuroautonomic dynamics in which interactions and "cross-talk" occur over a wide range of temporal and spatial scales, as well as for understanding complex pharmacologic effects. Fractal analysis of long DNA sequences has recently revealed that non-coding, but not coding sequences possess long-range correlations among nucleotides (64). This finding has implications for possible functions of introns as well as for understanding molecular evolution (65) and developing new methods for distinguishing coding from non-coding sections of long DNA sequences (66). Findings from nonlinear dynamics have also challenged conventional mechanisms of physiological control based on classical homeostasis, which presumes that healthy systems seek to attain a constant steady state. In contrast, nonlinear systems with fractal dynamics, such as the neuroautonomic mechanisms regulating heart rate variability, behave as if they were driven far from equilibrium under basal conditions. This kind of complex variability, rather than a regular homeostatic steady state, appears to define the free-running function of many biological systems (Fig. 1) (2, 39). Finally, a fundamental methodologic principle underlying these new applications and interpretations is the importance of analyzing continuously sampled variations in physiological output, such as heart rate, and not simply relying on averaged values or measures of variance. Dynamical analysis demonstrates that there is often hidden information in physiological time series and that certain fluctuations previously considered "noise" actually represent important signals (67-70).
1. Glass L, Mackey MC. From Clocks to Chaos: the Rhythms of Life. Princeton: University Press, 1988.
2. Goldberger AL, Rigney DR, West BJ. Chaos and fractals in human physiology. Sci Am 1990;262:42-9.
3. Bassingthwaighte JB, Liebovitch LS, West BJ. Fractal Physiology. New York: Oxford University Press, 1994.
4. Kaplan DT, Glass L. Understanding Nonlinear Dynamics. New York: Springer-Verlag, 1995.
5. May RM. Simple mathematical models with very complicated dynamical behavior. Nature 1976;261:459-67.
6. Winfree AT. When Time Breaks Down: The Three-Dimensional Dynamics of Electrochemical Waves and Cardiac Arrhythmias. Princeton, NJ: Princeton University Press, 1987.
7. Davidenko JM, Pertsov AV, Salomonosz R, Baxter W, Jalife J. Stationary and drifting waves of excitation in isolated cardiac muscle. Nature 1992;355:349-51.
8. Babloyantz A, Destexhe A. Low dimensional chaos in an instance of epilepsy. Proc Natl Acad Sci USA 1986;83:3513-7.
9. Goldberger AL, Rigney DR. Nonlinear dynamics at the bedside. In: Glass L, Hunter P, McCulloch A. eds. Theory of Heart. New York: Springer, 1991; 583-605.
10. Chay TR, Lee YS. Bursting, beating, and chaos by two functionally distinct inward current inactivations in excitable cells. Ann NY Acad Sci 1990;591:328-50.
11. Longtin A, Bulsara A, Pierson D, Moss F. Bistability and the dynamics of periodically forced sensory neurons. Biol Cybern 1994;70:569-78.
12. Delmar M, Ibarra J, Davidenko J, Lorente P, Jalife J. Dynamics of the background outward current of single guinea pig ventricular myocytes: ionic mechanisms of hysteresis in cardiac cells. Circ Res 1991;69:1316-26.
13. Courtemanche M, Glass L. Rosengarten MD, Goldberger AL. Beyond pure parasystole: promises and problems in modelling complex arrhythmias. Am J Physiol 1989; 257 (Heart Circ Physiol 26):H693-706.
14. Wiesenfeld K, Moss F. Stochastic resonance and the benefits of noise: from ice ages to crayfish and SQUIDS. Nature 1995;373:33-6.
15. Glass L, Guevara MR, Shrier A. Universal bifurcations and the classification of cardiac arrhythmias. Ann NY Acad Sci 1987;504:168-78.
16. Rigney DR, Goldberger AL. Nonlinear mechanics of the heart's swinging during pericardial effusion. Am J Physiol 1989;257(Heart Circ Physiol 20):H:1292-1305.
17. Rosenbaum DS, Jackson LE, Smith JM, Garan H, Ruskin JN, Cohen RJ. Electrical alternans and vulnerability to ventricular arrhythmia. N Engl J Med 1994;330:235-41.
18. Peterson I. Newton's Clock: Chaos in the Solar System. New York: WH Freeman, 1993.
19. Smith JM, Cohen RJ. Simple finite-element model accounts for wide range of cardiac dysrhythmias. Proc Natl Acad Sci USA 1984;81:233-7.
20. Goldberger AL, Bhargava V, West BJ, Mandell AJ. Some observations on the question: Is ventricular fibrillation "chaos?" Physica D 1986;19:282-9.
21. Kaplan DT, Cohen RJ. Is fibrillation chaos? Circ Res 1990;67:886-92.
22. Goldberger AL. Is the normal heartbeat chaotic or homeostatic? News in Physiological Science 1991;6:87-91.
23. Prank K, Harms H, Dammig M, Brabant G, Mitschke F, Hesch RD. Is there low-dimensional chaos in pulsatile secretion of parathyroid hormone in normal human subjects? Am J Physiol 1994;266:E653-8.
24. Kanters JK, Holstein-Rathlou NH, Anger E. Lack of evidence for low-dimensional chaos in heart rate variability. J Cardiovasc Electrophysiol 1994;5:591-601.
25. Elbert T, Ray WJ, Kowalik ZJ, Skinner JE, Graf KE, Birbaumer N. Chaos and physiology: deterministic chaos in excitable cell assemblies. Physiol Rev 1994;74:1-47.
26. Griffith TM, Edwards DH. Fractal analysis of role of smooth muscle Ca2+ fluxes in genesis of chaotic arterial pressure oscillations. Am J Physiol 1994;266:H1801-11.
27. Wagner CD, Persson PB. Nonlinear chaotic dynamics of arterial blood pressure and renal blood flow. Am J Physiol 1995;268:H621-7.
28. Sugihara G. Allan W, Sobel D, Alan KD. Nonlinear control of heart rate variability in human infants. Proc Natl Acad Sci USA 1996;93:2608-13.
29. Mandelbrot BB. The Fractal Geometry of Nature. New York: WH Freeman, 1982.
30. Weibel ER. Fractal geometry: a design principle for living organisms. Am J Physiol 1991;261(Lung Cell Mol Physiol 5):L361-369.
31. Abboud S, Berenfeld O, Sadeh D. Simulation of high-resolution QRS complex using a ventricular model with a fractal conduction system. Effects of ischemia on high-frequency QRS potentials. Circ Res 1991;68:1751-60.
32. Peskin CS, McQueen DM. Mechanical equilibrium determines the fractal fiber architecture of aortic heart valve leaflets. Am J Physiol 1994;266(Heart Circ. Physiol. 35):H319-28.
33. Shlesinger MF. Fractal time and 1/f noise in complex systems. Ann NY Acad Sci 1987;504:214-28.
34. Kobayashi M, Musha T. 1/f fluctuation of heartbeat period. IEEE Trans Biomed Eng 1982;29:456-7.
35. Peng C-K, Mietus J, Hausdorff JM, Havlin S, Stanley HE, Goldberger AL. Long-range anti-correlations and non-Gaussian behavior of the heartbeat. Phys Rev Lett 1993;70:1343-6.
36. Szeto H, Chen PY, Decena JA, Cheng YI, Wu Dun-L, Dwyer G. Fractal properties of fetal breathing dynamics. Am J Physiol 1992;263(Regulatory Interactive Comp Physiol. 32):R141-7.
37. Marsh DJ, Osborn JL, Cowley AW. 1/f fluctuations in arterial pressure and regulation of renal blood flow in dogs. Am J Physiol 1990;258:F1394-1400.
38. Hausdorff JM, Peng C-K, Ladin Z, Wei JY, Goldberger. Is walking a random walk? Evidence for long-range correlations in the stride interval of human gait. J Appl Physiol 1995;78:349-58.
38. Goldberger AL, Kobalter K, Bhargava V. 1/f-like scaling in normal neutrophil dynamics: Implications for hematologic monitoring. IEEE Trans Biomed Eng 1986;33:874-6.
39. Goldberger AL. Fractal variability versus pathologic periodicity: complexity loss and stereotypy in disease. Perspect Biol Med 1997;40:543-61.
40. Lipsitz LA, Goldberger AL. Loss of "complexity" and aging: potential applications of fractals and chaos theory to senescence. JAMA 1992;267:1806-9.
41. Goldberger AL, Findley LJ, Blackburn MR, Mandell AJ. Nonlinear dynamics in heart failure: implications of long-wavelength cardiopulmonary oscillations. Am Heart J 1984;107:612-5.
42. Freeman WJ. Role of chaotic dynamics in neural plasticity. Prog Brain Res 1994;102:319-33.
43. Goldberger AL, Rigney DR, Mietus J, Antman EM, Greenwald S. Nonlinear dynamics in sudden cardiac death syndrome: heartrate oscillations and bifurcations. Experientia 1988;44:983-7.
44. Saul JP, Arai Y, Berger RD, Lilly LS, Colucci WS, Cohen RJ. Assessment of autonomic regulation in chronic congestive heart failure by heart rate spectral analysis. Am J Cardiol 1988;61:1292-9.
45. Katz M, Meizner I, Shani N, Insler V. Clinical significance of sinusoidal heart rate pattern. Br J Obstet Gynaecol 1983;90:832-6.
46. Reimann HA. Periodic Diseases. Philadelphia: F.A. Davis Company, 1963.
47. Glass L, Mackey MC. Pathological conditions resulting from instabilities in physiological control systems. Ann NY Acad Sci 1978;316:214-35.
48. Mackey MC, Glass L. Oscillations and chaos in physiological control systems. Science 1977;197:287-9.
49. Milton J, Black D. Dynamic diseases in neurology and psychiatry. Chaos 1995;5:8-13.
50. Peng CK, Havlin S, Stanley HE, Goldberger AL. Quantification of scaling exponents and crossover phenomena in nonstationary hearbeat time series. Chaos 1995;5:82-7.
51. Nearing BD, Huang AH, Verrier RL. Dynamic tracking of cardiac vulnerability by complex demodulation of the T wave. Science 1991;252:437-40.
52. Ho KKL, Moody GB, Peng C-K, Mietus JE, Larson MG, Levy D, Goldberger AL. Predicting survival in heart failure cases and controls using fully automated methods for deriving nonlinear and conventional indices of heart rate dynamics. Circulation 1997;96:842-8.
53. Mäkikallio TH, Seppänen T, Airaksinen KEJ, Koistinen J, Tulppo MP, Peng C-K, Goldberger AL, Huikuri HV. Dynamic analysis of heart rate may predict subsequent ventricular tachycardia after myocardial infarction. Am J Cardiol 1997;80:779-83.
54. Mäkikallio TH, Ristimäe T, Airaksinen KEJ, Peng C-K, Goldberger AL, Huikuri HV. Heart rate dynamics in patients with stable angina pectoris and utility of fractal and complexity measures. Am J Cardiol 1998;81:27-31.
55. Mäkikallio TH, Hoiber S, Kober L, Torp-Pedersen C, Peng C-K, Goldberger AL, Huikuri HV. Fractal analysis of heart rate dynamics as a predictor of mortality in patients with depressed left ventricular function after acute myocardial infarction. Am J Cardiol 1999;83:836-9.
56. Amaral LAN, Goldberger AL, Ivanov PCh, Stanley HE. Scale-independent measures and pathologic cardiac dynamics. Phys Rev Lett 1998;81:2388-91.
57. Iyengar N, Peng C-K, Morin R, Goldberger AL, Lipsitz LA. Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics. Am J Physiol 1996;271:1078-84.
58. Hausdorff JM, Mitchell SL, Firtion R, Peng C-K, Cudkowicz ME, Wei JY, Goldberger AL. Altered fractal dynamics of gait: reduced stride interval correlations with aging and Huntington's disease. J Appl Physiol 1997;82:262-9.
59. Pincus SM, Goldberger AL. Physiological time-series analysis: what does regularity quantify? Am J Physiol 1994;266(Heart Circ Physiol):H1643-56.
60. Skinner JE, Carpeggiani C, Landesman CE, Fulton KW. The correlation-dimension of the heartbeat is reduced by myocardial ischemia in conscious pigs. Circ Res 1991;68:966-76.
61. Schiff SJ, Jerger K, Duong DH, Chang T, Spano ML, Ditto WL. Controlling chaos in the brain. Nature 1994;370:615-20.
62. Regalado A. A gentle scheme for unleashing chaos. Science 1995;268:1848.
63. Garfinkel A, Spano ML, Ditto WL, Weiss JN. Controlling cardiac chaos. Science 1992;257:1230-5.
64. Peng CK, Buldyrev SV, Goldberger AL, et al. Long-range correlations in nucleotide sequences. Nature 1992;356:168-70.
65. Buldyrev SV, Goldberger AL, Havlin S, Peng CK, Stanley HE, Simons M. Fractal landscapes and molecular evolution: modeling the myosin heavy chain gene family. Biophys J 1993;65:2673-9.
66. Ossadnik SM, Buldyrev SV, Goldberger AL, et al. Correlation approach to identify coding regions in DNA sequences. Biophys J 1994;67:64-70.
67. Ivanov PCh, Amaral LAN, Goldberger AL, Stanley HE. Stochastic feedback and the regulation of biological rhythms. Europhys Lett 1998;43:363-8.
68. Ivanov PCh, Rosenblum MG, Peng C-K, Mietus J, Havlin S, Stanley HE, Goldberger AL. Scaling behavior of heartbeat intervals obtained by wavelet-based time series analysis. Nature 1996;383:323-7.
69. Ivanov PCh, Amaral LAN, Goldberger AL, Havlin S, Rosenblum MG, Struzik Z, Stanley HE. Multifractality in human heartbeat dynamics. Nature 1999;399:461-5.
70. Peng C-K, Hausdorff JM, Goldberger AL. Fractal mechanisms in neural control: human heartbeat and gait dynamics in health and disease. In: Walleczek J, ed. Self-Organized Biological Dynamics and Nonlinear Control. Cambridge: Cambridge University Press, 1999.