Nonlinear control of heart rate variability in human infants.

Sugihara, George; Allan, Walter C.; Sobel, D; Allan, K D

doi:10.1073/pnas.93.6.2608

Cited by 144 publications

(95 citation statements)

References 37 publications

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“…Compared to the healthy subjects, the magnitude exhibits weaker correlations with a scaling exponent closer to the exponent of an uncorrelated series. The change in the magnitude exponent for the heart failure subjects is consistent with a previously reported loss of nonlinearity with disease [18,19,20,21]. The sign time series of heart failure subjects shows scaling behavior similar to the one observed in the original time series, but significantly different from the healthy subjects (Table 1).…”

supporting

confidence: 78%

Quantifying Heartbeat Dynamics by Magnitude and Sign Correlations

Ivanov¹,

Ashkenazy²,

Kantelhardt³

et al. 2003

AIP Conference Proceedings

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Abstract. We review a recently developed approach for analyzing time series with long-range correlations by decomposing the signal increment series into magnitude and sign series and analyzing their scaling properties. We show that time series with identical long-range correlations can exhibit different time organization for the magnitude and sign. We apply our approach to series of time intervals between consecutive heartbeats. Using the detrended fluctuation analysis method we find that the magnitude series is long-range correlated, while the sign series is anticorrelated and that both magnitude and sign series may have clinical applications. Further, we study the heartbeat magnitude and sign series during different sleep stages -light sleep, deep sleep, and REM sleep. For the heartbeat sign time series we find short-range anticorrelations, which are strong during deep sleep, weaker during light sleep and even weaker during REM sleep. In contrast, for the heartbeat magnitude time series we find long-range positive correlations, which are strong during REM sleep and weaker during light sleep. Thus, the sign and the magnitude series provide information which is also useful for distinguishing between different sleep stages.A broad class of physical and biological systems exhibits complex dynamics, associated with the presence of many components interacting over a wide range of time or space scales. These often-competing interactions may generate an output signal with fluctuations that appear "noisy" and "erratic" but reveal scale-invariant structure. One general approach to study these systems is to analyze the ways that such fluctuations obey scaling laws [1,2,3].We consider the time series formed by consecutive cardiac interbeat intervals (Fig. 1a) and focus on the correlations in the time increments between consecutive beats. This time series is of general interest, in part because it is the output of a complex integrated control system, including competing stimuli from the neuroautonomic nervous system [4]. These stimuli modulate the rhythmicity of the heart's intrinsic pacemaker, leading to complex fluctuations. Previous reports indicate that these fluctuations exhibit scaleinvariant properties [5,6,7], and are anticorrelated over a broad range of time scales (i.e., the power spectrum follows a power-law where the amplitudes of the higher frequencies are dominant) [8]. By long-range anticorrelations we also mean that the root mean square fluctuations function of the integrated series is proportional to n α where n is the window scale and the scaling exponent α is smaller than 0.5. In contrast, for uncorrelated behavior α 0 5, while for correlated behavior α 0 5.The time series of the fluctuations in heartbeat intervals can be "decomposed" into

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supporting

confidence: 78%

Quantifying Heartbeat Dynamics by Magnitude and Sign Correlations

Ivanov¹,

Ashkenazy²,

Kantelhardt³

et al. 2003

AIP Conference Proceedings

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“…1B) suggests that different parts of the signal may have different scaling properties. In addition, evidence that heartbeat dynamics exhibit nonlinear properties (41)(42)(43)(44) indicates the need to study higher order correlations. To obtain additional information about the singular properties of such signals, we recently adapted the wavelet modulus maxima method to physiologic time series (14).…”

Section: Beyond 1͞f Noise: Multifractal Scaling In Heartbeat 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.

1,791

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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“…The lack of a definitive test of chaos in experimental time series has thwarted the application of nonlinear dynamics theory (4,11) to a variety of physical (6,(12)(13)(14)(15), biomedical (16)(17)(18)(19)(20)(21)(22)(23)(24), and socioeconomic systems (25)(26)(27)(28)(29)(30) where chaos is thought to play a role. Overcoming these hurdles may open exciting possibilities for practical applications such as the improved forecasting of weather (31) and economic (30) patterns, novel strategies for diagnosis and control of pathological states in biomedicine (23,24,(32)(33)(34)(35) or the unmasking of chaotically encrypted electronic or optical communication signals (36)(37)(38)(39).…”

mentioning

confidence: 99%

Titration of chaos with added noise

Poon

Barahona

2001

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

135

114

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Deterministic chaos has been implicated in numerous natural and man-made complex phenomena ranging from quantum to astronomical scales and in disciplines as diverse as meteorology, physiology, ecology, and economics. However, the lack of a definitive test of chaos vs. random noise in experimental time series has led to considerable controversy in many fields. Here we propose a numerical titration procedure as a simple ''litmus test'' for highly sensitive, specific, and robust detection of chaos in short noisy data without the need for intensive surrogate data testing. We show that the controlled addition of white or colored noise to a signal with a preexisting noise floor results in a titration index that: (i) faithfully tracks the onset of deterministic chaos in all standard bifurcation routes to chaos; and (ii) gives a relative measure of chaos intensity. Such reliable detection and quantification of chaos under severe conditions of relatively low signal-to-noise ratio is of great interest, as it may open potential practical ways of identifying, forecasting, and controlling complex behaviors in a wide variety of physical, biomedical, and socioeconomic systems.

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Nonlinear control of heart rate variability in human infants.

Cited by 144 publications

References 37 publications

Quantifying Heartbeat Dynamics by Magnitude and Sign Correlations

Quantifying Heartbeat Dynamics by Magnitude and Sign Correlations

Fractal dynamics in physiology: Alterations with disease and aging

Titration of chaos with added noise

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