Power Law Behavior of RR-Interval Variability in Healthy Middle-Aged Persons, Patients With Recent Acute Myocardial Infarction, and Patients With Heart Transplants

Bigger, J. Thomas; Steinman, Richard C.; Rolnitzky, Linda; Fleiss, Joseph L.; Albrecht, Paul; Cohen, Richard J.

doi:10.1161/01.cir.93.12.2142

Cited by 332 publications

(275 citation statements)

References 14 publications

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“…The importance of the autonomic nervous system in determining the power-law slope is supported by the observation that denervated hearts have a substantially steeper slope (Bigger et al 1996). However, the physiological background of this measure is not well established.…”

Section: Fractal Measures Of Hrvmentioning

confidence: 99%

Clinical impact of evaluation of cardiovascular control by novel methods of heart rate dynamics

Huikuri

Perkiömäki

Maestri

et al. 2009

Phil. Trans. R. Soc. A.

165

124

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Heart rate variability (HRV) has been conventionally analysed with time-and frequencydomain methods, which measure the overall magnitude of RR interval fluctuations around its mean value or the magnitude of fluctuations in some predetermined frequencies. Analysis of heart rate dynamics by novel methods, such as heart rate turbulence after ventricular premature beats, deceleration capacity of heart rate and methods based on chaos theory and nonlinear system theory, have gained recent interest. Recent observational studies have suggested that some indices describing nonlinear heart rate dynamics, such as fractal scaling exponents, heart rate turbulence and deceleration capacity, may provide useful prognostic information in various clinical settings and their reproducibility may be better than that of traditional indices. For example, the short-term fractal scaling exponent measured by the detrended fluctuation analysis method has been shown to predict fatal cardiovascular events in various populations. Similarly, heart rate turbulence and deceleration capacity have performed better than traditional HRV measures in predicting mortality in post-infarction patients. Approximate entropy, a nonlinear index of heart rate dynamics, which describes the complexity of RR interval behaviour, has provided information on the vulnerability to atrial fibrillation. There are many other nonlinear indices which also give information on the characteristics of heart rate dynamics, but their clinical usefulness is not as well established. Although the concepts of nonlinear dynamics, fractal mathematics and complexity measures of heart rate behaviour, heart rate turbulence, deceleration capacity in relation to cardiovascular physiology or various cardiovascular events are still far away from clinical medicine, they are a fruitful area for research to expand our knowledge concerning the behaviour of cardiovascular oscillations in normal healthy conditions as well as in disease states.

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Section: Fractal Measures Of Hrvmentioning

confidence: 99%

Clinical impact of evaluation of cardiovascular control by novel methods of heart rate dynamics

Huikuri

Perkiömäki

Maestri

et al. 2009

Phil. Trans. R. Soc. A.

165

124

View full text Add to dashboard Cite

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“…Based on their potential to give additional information beyond traditional heart rate variability (HRV) indices [1], nonlinear parameters have been applied for investigating short and long term effects of exercise on heart rate (HR) control [5][6][7][8]. However, despite their diagnosticity and their clinical significance [9][10][11][12][13][14][15][16], the physiological background of their behavior is not very well established. It is assumed that complexity and regularity measures are fundamentally different from traditional HRV indices [17] and show no correlation to these measures [12,18].…”

Section: Introductionmentioning

confidence: 99%

Sample Entropy and Traditional Measures of Heart Rate Dynamics Reveal Different Modes of Cardiovascular Control During Low Intensity Exercise

Weippert

Behrens

Rieger

et al. 2014

Entropy

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Nonlinear parameters of heart rate variability (HRV) have proven their prognostic value in clinical settings, but their physiological background is not very well established. We assessed the effects of low intensity isometric (ISO) and dynamic (DYN) exercise of the lower limbs on heart rate matched intensity on traditional and entropy measures of HRV. Due to changes of afferent feedback under DYN and ISO a distinct autonomic response, mirrored by HRV measures, was hypothesized. Five-minute inter-beat interval measurements of 43 healthy males (26.0 ± 3.1 years) were performed during rest, DYN and ISO in a randomized order. Blood pressures and rate pressure product were higher during ISO vs. DYN (p < 0.001). HRV indicators SDNN as well as low and high frequency power were significantly higher during ISO (p < 0.001 for all measures). Compared to DYN, sample entropy (SampEn) was lower during ISO (p < 0.001). Concluding, contraction mode itself is a significant modulator of the autonomic cardiovascular response to exercise. Compared to DYN, ISO evokes a stronger blood pressure response and an enhanced interplay between both autonomic branches. Non-linear HRV measures indicate a more regular behavior under ISO. Results support the view of the reciprocal antagonism being OPEN ACCESSEntropy 2014, 16 5699 only one of many modes of autonomic heart rate control. Under different conditions; the identical "end product" heart rate might be achieved by other modes such as sympathovagal co-activation as well.

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“…Despite extensive studies on HRV using chaos theory, [1][2][3][4][5][6][7][8][9][10] fractal scaling analysis, [11][12][13][14][15] and many other methods in the last two decades, the issue of whether HRV is chaotic or stochastic remains highly controversial. The debate can hardly be settled if one does not go beyond the standard theories of chaos and random fractals, since the foundations for the two theories are different: chaos theory is mainly concerned about apparently irregular behaviors in a complex system that are generated by nonlinear deterministic interactions with only a few degrees of freedom, where noise or intrinsic randomness does not play an important role, while random fractal theory assumes that the dynamics of the system are inherently random.…”

Section: Introductionmentioning

confidence: 99%

Characterizing heart rate variability by scale-dependent Lyapunov exponent

Hu¹,

Gao²,

Tung

2009

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Hu, J; Gao, J.; and Tung, W., "Characterizing heart rate variability by scale-dependent Lyapunov exponent" (2009) Previous studies on heart rate variability ͑HRV͒ using chaos theory, fractal scaling analysis, and many other methods, while fruitful in many aspects, have produced much confusion in the literature. Especially the issue of whether normal HRV is chaotic or stochastic remains highly controversial. Here, we employ a new multiscale complexity measure, the scale-dependent Lyapunov exponent ͑SDLE͒, to characterize HRV. SDLE has been shown to readily characterize major models of complex time series including deterministic chaos, noisy chaos, stochastic oscillations, random 1 / f processes, random Levy processes, and complex time series with multiple scaling behaviors. Here we use SDLE to characterize the relative importance of nonlinear, chaotic, and stochastic dynamics in HRV of healthy, congestive heart failure, and atrial fibrillation subjects. We show that while HRV data of all these three types are mostly stochastic, the stochasticity is different among the three groups. © 2009 American Institute of Physics. ͓DOI: 10.1063/1.3152007͔Determining whether heartbeat dynamics is chaotic or stochastic is an important issue, both theoretically and clinically. The problem is difficult to solve neatly, however, since heart rate variability (HRV) may exhibit both nonlinear, and possibly chaotic, as well as stochastic behaviors. This motivates us to employ a recently developed multiscale complexity measure, the scale-dependent Lyapunov exponent (SDLE), to characterize HRV. SDLE cannot only unambiguously distinguish chaos from noise but also characterize various types of complex time series. Using SDLE, we are able to quantify the relative importance of nonlinear, chaotic, and stochastic dynamics in HRV of healthy, congestive heart failure, and atrial fibrillation subjects.

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Power Law Behavior of RR-Interval Variability in Healthy Middle-Aged Persons, Patients With Recent Acute Myocardial Infarction, and Patients With Heart Transplants

Cited by 332 publications

References 14 publications

Clinical impact of evaluation of cardiovascular control by novel methods of heart rate dynamics

Clinical impact of evaluation of cardiovascular control by novel methods of heart rate dynamics

Sample Entropy and Traditional Measures of Heart Rate Dynamics Reveal Different Modes of Cardiovascular Control During Low Intensity Exercise

Characterizing heart rate variability by scale-dependent Lyapunov exponent

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