Multifractal characterization of blood pressure dynamics: stress-induced phenomena

Pavlov, Alexey N.; Ziganshin, Amir R.; Klimova, Oksana A.

doi:10.1016/j.chaos.2004.09.025

Cited by 12 publications

(7 citation statements)

References 23 publications

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“…Values of the spectrum h < 0.5 correspond to anti-persistent or negatively correlated behaviour and h > 0.5 to persistent or positively correlated behaviour. Singularity spectra with non-zero widths, indicative of multifractal dynamics, have been measured in electrocardiographic (ECG) signals (Ivanov et al, 1999, 2001; Chiu et al, 2007; Wang et al, 2007; Pavlov et al, 2005), human gait recordings (West and Latka, 2005), electroencephalographic (EEG) signals (Song et al, 2005), and functional magnetic resonance imaging (fMRI) time series (Shimizu et al, 2004). Studies of multifractal properties of ECG signals especially have shown that the maximum (peak) value and width of the singularity spectrum are affected by disease (cardiac failure) and ageing, confirming that these parameters are sensitive to dynamic departures from health.…”

Section: Introductionmentioning

confidence: 99%

Endogenous multifractal brain dynamics are modulated by age, cholinergic blockade and cognitive performance

Suckling

Wink

Bernard³

et al. 2008

Journal of Neuroscience Methods

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The intuitive notion that a healthy organism is characterised by regular, mechanistic function has been challenged by observations that a loss complexity is, in fact, indicative of ill-health. Monofractals succinctly describe complex processes, and are controlled by a single time-invariant scaling exponent, H, simply related to the fractal dimension. A previous analysis of resting fMRI time-series demonstrated that ageing and scopolamine administration were both associated with increases in H and that faster response in a prior encoding task was also associated with increased H. We revisit this experiment with a novel regional, multifractal approach in which fractal dynamics are assumed non-stationary and defined by a spectrum of local singularity exponents. Parameterisation of this spectrum was capable of refracting the effects of age, scopolamine and task performance as well as a refining a description of the associated signal changes. Using the same imaging data, we also explored turbulence as a possible mechanism underlying multifractal dynamics. Evidence is provided that Carstaing's model of turbulent information flow from high to low scales has only limited validity, and that invariance of energy dissipation is better explained by critical-phase phenomena, supporting the proposition that the brain maintains a state of self-organised criticality.

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Section: Introductionmentioning

confidence: 99%

Endogenous multifractal brain dynamics are modulated by age, cholinergic blockade and cognitive performance

Suckling

Wink

Bernard³

et al. 2008

Journal of Neuroscience Methods

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“…Conceptually, the wavelet transform is the convolution product of the data series with the scaled and translated wavelet functions. On the basis of the appropriate choice of the analyzing wavelet, the transform can be made blind to polynomial trends [ Pavlov et al , 2005]. To this end, the number of vanishing moments for the wavelet basis is chosen to match the order of polynomial trends in the data series.…”

Section: Theorymentioning

confidence: 99%

Wavelet‐based multifractal analysis of field scale variability in soil water retention

Zeleke

Cheng

2007

Water Resources Research

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[1] Better understanding of spatial variability of soil hydraulic parameters and their relationships to other soil properties is essential to scale-up measured hydraulic parameters and to improve the predictive capacity of pedotransfer functions. The objective of this study was to characterize scaling properties and the persistency of water retention parameters and soil physical properties. Soil texture, bulk density, organic carbon content, and the parameters of the van Genuchten water retention function were determined on 128 soil cores from a 384-m transect with a sandy loam soil, located at Smeaton, SK, Canada. The wavelet transform modulus maxima, or WTMM, technique was used in the multifractal analysis. Results indicate that the fitted water retention parameters had higher small-scale variability and lower persistency than the measured soil physical properties. Of the three distinct scaling ranges identified, the middle region (8-128 m) had a multifractal-type scaling. The generalized Hurst exponent indicated that the measured soil properties were more persistent than the fitted soil hydraulic parameters. The relationships observed here imply that soil physical properties are better predictors of water retention values at larger spatial scales than at smaller scales.

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“…MSE, based on SampEn, takes into account the correlations inherent in biological signals at multiple time scales. Multifractality is present in HRV [11], [12], blood pressure dynamics [13] and electroencephalography (EEG) [14], [15]. Although the MSE analysis was derived from stationary processes, only those non-stationarities on scales much larger than those considered for the MSE analysis may affect the consistency of the results in practice [16].…”

Section: Introductionmentioning

confidence: 99%

Correlations between the Signal Complexity of Cerebral and Cardiac Electrical Activity: A Multiscale Entropy Analysis

Lin

Tsao

et al. 2014

PLoS ONE

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The heart begins to beat before the brain is formed. Whether conventional hierarchical central commands sent by the brain to the heart alone explain all the interplay between these two organs should be reconsidered. Here, we demonstrate correlations between the signal complexity of brain and cardiac activity. Eighty-seven geriatric outpatients with healthy hearts and varied cognitive abilities each provided a 24-hour electrocardiography (ECG) and a 19-channel eye-closed routine electroencephalography (EEG). Multiscale entropy (MSE) analysis was applied to three epochs (resting-awake state, photic stimulation of fast frequencies (fast-PS), and photic stimulation of slow frequencies (slow-PS)) of EEG in the 1–58 Hz frequency range, and three RR interval (RRI) time series (awake-state, sleep and that concomitant with the EEG) for each subject. The low-to-high frequency power (LF/HF) ratio of RRI was calculated to represent sympatho-vagal balance. With statistics after Bonferroni corrections, we found that: (a) the summed MSE value on coarse scales of the awake RRI (scales 11–20, RRI-MSE-coarse) were inversely correlated with the summed MSE value on coarse scales of the resting-awake EEG (scales 6–20, EEG-MSE-coarse) at Fp2, C4, T6 and T4; (b) the awake RRI-MSE-coarse was inversely correlated with the fast-PS EEG-MSE-coarse at O1, O2 and C4; (c) the sleep RRI-MSE-coarse was inversely correlated with the slow-PS EEG-MSE-coarse at Fp2; (d) the RRI-MSE-coarse and LF/HF ratio of the awake RRI were correlated positively to each other; (e) the EEG-MSE-coarse at F8 was proportional to the cognitive test score; (f) the results conform to the cholinergic hypothesis which states that cognitive impairment causes reduction in vagal cardiac modulation; (g) fast-PS significantly lowered the EEG-MSE-coarse globally. Whether these heart-brain correlations could be fully explained by the central autonomic network is unknown and needs further exploration.

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Multifractal characterization of blood pressure dynamics: stress-induced phenomena

Cited by 12 publications

References 23 publications

Endogenous multifractal brain dynamics are modulated by age, cholinergic blockade and cognitive performance

Endogenous multifractal brain dynamics are modulated by age, cholinergic blockade and cognitive performance

Wavelet‐based multifractal analysis of field scale variability in soil water retention

Correlations between the Signal Complexity of Cerebral and Cardiac Electrical Activity: A Multiscale Entropy Analysis

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