On the fractal nature of heart rate variability in humans: effects of data length and beta-adrenergic blockade

Yamamoto, Yoshiharu; Hughson, R. L.

doi:10.1152/ajpregu.1994.266.1.r40

Cited by 141 publications

(164 citation statements)

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“…The same issues of signal length, nonstationarity, influences of noise, and whether or not a single process is the basis of the signal (as opposed to a multicomponent signal) apply to chaotic or fractal signals. The technique used by Yamamato et al (27,28,29), called coarse graining spectral analysis, ought in principle to be simpler than defining a mixture of two fractal signals or the combination of a chaotic and fractal signal; they attempt to separate a simple periodic signal from a fractal signal. An assessment of this technique would also include the influences of signal length, relative amplitudes of fractal and periodic components, and of white noise.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of the dispersional analysis method for fractal time series

1995

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Fractal signals can be characterized by their fractal dimension plus some measure of their variance at a given level of resolution. The Hurst exponent, H, is <0.5 for rough anticorrelated series, >0.5 for positively correlated series, and =0.5 for random, white noise series. Several methods are available: dispersional analysis, Hurst rescaled range analysis, autocorrelation measures, and power special analysis. Short data sets are notoriously difficult to characterize; research to define the limitations of the various methods is incomplete. This numerical study of fractional Brownian noise focuses on determining the limitations of the dispersional analysis method, in particular, assessing the effects of signal length and of added noise on the estimate of the Hurst coefficient, H, (which ranges from 0 to 1 and is 2 − D, where D is the fractal dimension). There are three general conclusions: (i) pure fractal signals of length greater than 256 points give estimates of H that are biased but have standard deviations less than 0.1; (ii) the estimates of H tend to be biased toward H = 0.5 at both high H (>0.8) and low H (<0.5), and biases are greater for short time series than for long; and (iii) the addition of Gaussian noise (H = 0.5) degrades the signals: for those with negative correlation (H < 0.5) the degradation is great, the noise has only mild degrading effects on signals with H > 0.6, and the method is particularly robust for signals with high H and long series, where even 100% noise added has only a few percent effect on the estimate of H.Dispersional analysis can be regarded as a strong method for characterizing biological or natural time series, which generally show long-range positive correlation.

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Section: Discussionmentioning

confidence: 99%

Evaluation of the dispersional analysis method for fractal time series

1995

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“…The fractal concept can be applied not just to irregular geometric forms that lack a characteristic (single) scale of length, but also to certain complex processes that lack a single scale of time (17,19,22,23). Fractal processes generate irregular fluctuations across multiple time scales, analogous to scale-invariant objects that have a branching or wrinkly structure across multiple length scales.…”

Section: Fractal Anatomies and Self-similar Dynamicsmentioning

confidence: 99%

Fractal dynamics in physiology: Alterations with disease and aging

Goldberger

Amaral

Hausdorff

et al. 2002

Proc. Natl. Acad. Sci. U.S.A.

1,787

1,384

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According to classical concepts of physiologic control, healthy systems are self-regulated to reduce variability and maintain physiologic constancy. Contrary to the predictions of homeostasis, however, the output of a wide variety of systems, such as the normal human heartbeat, fluctuates in a complex manner, even under resting conditions. Scaling techniques adapted from statistical physics reveal the presence of long-range, power-law correlations, as part of multifractal cascades operating over a wide range of time scales. These scaling properties suggest that the nonlinear regulatory systems are operating far from equilibrium, and that maintaining constancy is not the goal of physiologic control. In contrast, for subjects at high risk of sudden death (including those with heart failure), fractal organization, along with certain nonlinear interactions, breaks down. Application of fractal analysis may provide new approaches to assessing cardiac risk and forecasting sudden cardiac death, as well as to monitoring the aging process. Similar approaches show promise in assessing other regulatory systems, such as human gait control in health and disease. Elucidating the fractal and nonlinear mechanisms involved in physiologic control and complex signaling networks is emerging as a major challenge in the postgenomic era.A hallmark of physiologic systems is their extraordinary complexity. The nonstationarity and nonlinearity of signals ( Fig. 1) generated by living organisms defy traditional mechanistic approaches based on homeostasis and conventional biostatistical methodologies. Recognition that physiologic time series contain ''hidden information'' has fueled growing interest in applying concepts and techniques from statistical physics, including chaos theory, to a wide range of biomedical problems from molecular to organismic levels (1, 2).This presentation describes one area of investigation that has engaged our collaborative attention, namely, fractal analysis of physiologic time series in health and disease. The discussion will focus primarily on certain features of the human heartbeat, one important example of complex physiologic fluctuations. The dynamics of another physiologic control system-human gait-is also briefly discussed. Recognizing that this topic represents only one selected aspect of the broad and rapidly expanding applications of complexity theory to biomedicine (Table 1), readers are referred to a number of useful reviews and references therein (1, 3-10).A motivating problem for our work is depicted in Fig. 1, which presents a dynamical self-test. Shown are 30-min heart rate time series from four subjects. Only one is from a healthy individual; the other three are from patients with life-threatening forms of heart disease. The problem is to identify the normal record. The (perhaps nonintuitive) answer to this ''test'' is given in the figure caption. Beyond its obvious diagnostic import, the problem of classifying temporal assays of integrated cardiac physiology has implications for understanding a...

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“…However, the position of the peak in D(h) during sympathetic blockade is not substantially modified from its position for the same subjects when given a placebo. This suggests that sympathetic blockade may not have a major effect on the linear correlations in the dynamics, that is, it does not change the average Hurst exponent substantially [20].…”

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confidence: 96%

Behavioral-Independent Features of Complex Heartbeat Dynamics

et al. 2001

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We test whether the complexity of cardiac interbeat interval time series is simply a consequence of the wide range of scales characterizing human behavior, especially physical activity, by analyzing data taken from healthy adult subjects under three conditions with controls: (i) a "constant routine" protocol where physical activity and postural changes are kept to a minimum, (ii) sympathetic blockade, and (iii) parasympathetic blockade. We find that when fluctuations in physical activity and other behavioral modifiers are minimized, a remarkable level of complexity of heartbeat dynamics remains, while for neuroautonomic blockade the multifractal complexity decreases.

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On the fractal nature of heart rate variability in humans: effects of data length and beta-adrenergic blockade

Cited by 141 publications

References 0 publications

Evaluation of the dispersional analysis method for fractal time series

Evaluation of the dispersional analysis method for fractal time series

Fractal dynamics in physiology: Alterations with disease and aging

Behavioral-Independent Features of Complex Heartbeat Dynamics

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