Traditional and Nonlinear Heart Rate Variability Are Each Independently Associated with Mortality after Myocardial Infarction

Stein, Phyllis K.; Domitrovich, Peter P.; Huikuri, Heikki V.; Kleiger, Robert E.

doi:10.1046/j.1540-8167.2005.04358.x

Cited by 254 publications

(216 citation statements)

References 37 publications

(84 reference statements)

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“…Loss of the scale-invariance may indicate a simpler physiological control system that is less adaptive to perturbations and which is more vulnerable to catastrophic events (Huikuri et al, 2000;Perkiomaki et al, 2001). For instance, reduced scale-invariant correlations of heartbeat intervals occurs with cardiovascular disease and this is associated with reduced survival (Peng et al, 1995;Ashkenazy et al, 2001;Goldberger et al, 2002;Stein et al, 2005). However, until now such behavior has defied explanation in terms of neural mechanisms.…”

Section: Quantification Of Scale Invariance Over Different Time Scalementioning

confidence: 99%

The suprachiasmatic nucleus functions beyond circadian rhythm generation

et al. 2007

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We recently discovered that human activity possesses a complex temporal organization characterized by scale-invariant/self-similar fluctuations from seconds to ~4 hour-statistical properties of fluctuations remain the same at different time scales. Here, we show that scale-invariant activity patterns are essentially identical in humans and rats, and exist for up to ~24 hour: six-times longer than previously documented. Theoretically, such scale-invariant patterns can be produced by a neural network of interacting control nodes-system components with feedback loops-operating at different time scales. However such control nodes have not yet been identified in any neurophysiological model of scale invariance/self-similarity in mammals. Here we demonstrate that the endogenous circadian pacemaker (suprachiasmastic nucleus; SCN), known to modulate locomotor activity with a periodicity of ~24 hour, also acts as a major neural control node responsible for the generation of scale-invariant locomotor patterns over a broad range of time scales from minutes to at least 24 hour (rather than solely at ~24 hour). Remarkably, we found that SCN lesion in rats completely abolished the scale-invariant locomotor pattern between 4 and 24 hour and significantly altered the patterns at time scales <4 hour. Identification of the control nodes of a neural network responsible for scale invariance is the critical first step in understanding the neurophysiological origin of scale invariance/self-similarity. KeywordsActivity; Scale-invariance; Suprachiasmatic nucleus lesion; Human; Rat; Network The suprachiasmastic nucleus (SCN) in mammals generates self-sustained oscillations that orchestrate a wide range of neurophysiologic functions-from cellular processes to overt behaviors-with a time period of ~24 hour (Weaver, 1998). One of the fundamental processes influenced by the SCN is motor activity, which displays clear ~24 hour rhythms even under

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Section: Quantification Of Scale Invariance Over Different Time Scalementioning

confidence: 99%

The suprachiasmatic nucleus functions beyond circadian rhythm generation

et al. 2007

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“…Both goals are, of course, closely related. Considerable effort has gone into methods based on the assumption that, in view of the complexity of the activity of the autonomous nervous system, at least a major part of the variability of the heart rate may treated as a noise 4 driven process [6,7,8]. Most of these methods use fractal or multifractal scaling analysis [9].…”

Section: Introductionmentioning

confidence: 99%

How random is your heart beat?

Urbanowicz¹,

Żebrowski²,

Baranowski³

et al. 2007

Physica A: Statistical Mechanics and its Applications

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Abstract:We measure the content of random uncorrelated noise in heart rate variability using a general method of noise level estimation using a coarse grained entropy. We show that usually -except for atrial fibrillation -the level of such noise is within 5 -15% of the variance of the data and that the variability due to the linearly correlated processes is dominant in all cases analysed but atrial fibrillation. The nonlinear deterministic content of heart rate variability remains significant and may not be ignored.

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“…Laitio et al [23] showed that an increased SD1/SD2 ratio was the most powerful predictor of postoperative ischemia. Stein et al [87] also demonstrated that an increased SD1/SD2 ratio had the strongest association with mortality. Further, Mäkikallio et al [88] found for healthy subjects SD1/SD2 0.2, for post infarction patients with ventricular tachyarrhythmia SD1/SD2 0.3 and for patients with a previous myocardial infarction who had experienced ventricular fibrillation SD1/SD2 0.6.…”

Section: Poincaré Plot Analysesmentioning

confidence: 94%

Complex and Nonlinear Analysis of Heart Rate Variability in the Assessment of Fetal and Neonatal Wellbeing

Signorini

Magenes

Ferrario

et al. 2017

Complexity and Nonlinearity in Cardiovascular Signals

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Methods from nonlinear dynamics have shown new insights into alterations of the cardiovascular system under various physiological and pathological conditions, and thus providing additional prognostic information. In this chapter prominent complexity methods of non-linear dynamics as symbolic dynamics, Poincaré plot analyses, and compression entropy are introduced and their algorithmic implementations and application examples in clinical trials are provided. Especially, we will give their basic theoretical background, their main features and demonstrate their usefulness in different applications in the field of cardiovascular and cardiorespiratory time series analyses. IntroductionLinear time and frequency domain measures are often not sufficient enough to quantify the complex dynamics of physiological systems and their related time series. Therefore, various efforts have been made to apply nonlinear complexity measures to analyze, e.g. the heart rate variability (HRV) [1]. These approaches differ from the traditional time-and frequency domain HRV analyses because they quantify the signal properties instead of assessing only the magnitude or the frequency power of the heart rate time series. They assess the self-affinity of heartbeat fluctuations over multiple time scales (fractal measures); the regularity/irregularity or randomness of heartbeat fluctuations (entropy measures); the coarse-grained dynamics of HR fluctuations based on symbols (symbolic dynamics) and the heartbeat dynamics based on a simplified phase-space embedding [1].Symbolic dynamics is based on a coarse graining of the dynamics of a signal. The time series (in our cases the ECG or the noninvasively recorded blood pressure curve) are transformed into symbol sequences with symbols of a given alphabet. Some detail information is lost but the coarse dynamic behavior retains and

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Traditional and Nonlinear Heart Rate Variability Are Each Independently Associated with Mortality after Myocardial Infarction

Abstract: Nonlinear HRV is associated with mortality post-MI. However, as with traditional HRV, this is diluted by CABG surgery post-MI and by diabetes. Results suggest that decreased long-term HRV and increased randomness of heart rate are each independent risk factors for mortality post-MI.

Cited by 254 publications

References 37 publications

The suprachiasmatic nucleus functions beyond circadian rhythm generation

The suprachiasmatic nucleus functions beyond circadian rhythm generation

How random is your heart beat?

Complex and Nonlinear Analysis of Heart Rate Variability in the Assessment of Fetal and Neonatal Wellbeing

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