Copula as a dynamic measure of cardiovascular signal interactions

Jovanovic, Sladjana; Škorić, Tamara; Šarenac, Olivera; Milutinović-Smiljanić, Sanja; Japundžić‐Žigon, Nina; Bajić, Dragana

doi:10.1016/j.bspc.2018.03.007

Cited by 16 publications

(13 citation statements)

References 30 publications

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“…Medical applications include aortic regurgitation study [ 44 ] and diagnostic classifiers for neuropsychiatric disorders [ 45 ]. A possibility to use a bivariate copula to analyze the cardiovascular dependency structures was introduced in [ 46 ] and pharmacologically validated by blocking the feedback paths using Scopolamine, Atenolol, Prazosin, and Hexamethonium. It was shown that Frank’s copula is the most appropriate to quantify the level of dependency of cardiovascular signals.…”

Section: Methodsmentioning

confidence: 99%

Voronoi Decomposition of Cardiovascular Dependency Structures in Different Ambient Conditions: An Entropy Study

Bajić

Škorić

Milutinović-Smiljanić

et al. 2019

Entropy

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This paper proposes a method that maps the coupling strength of an arbitrary number of signals D, D ≥ 2, into a single time series. It is motivated by the inability of multiscale entropy to jointly analyze more than two signals. The coupling strength is determined using the copula density defined over a [0 1]D copula domain. The copula domain is decomposed into the Voronoi regions, with volumes inversely proportional to the dependency level (coupling strength) of the observed joint signals. A stream of dependency levels, ordered in time, creates a new time series that shows the fluctuation of the signals’ coupling strength along the time axis. The composite multiscale entropy (CMSE) is then applied to three signals, systolic blood pressure (SBP), pulse interval (PI), and body temperature (tB), simultaneously recorded from rats exposed to different ambient temperatures (tA). The obtained results are consistent with the results from the classical studies, and the method itself offers more levels of freedom than the classical analysis.

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Section: Methodsmentioning

confidence: 99%

Voronoi Decomposition of Cardiovascular Dependency Structures in Different Ambient Conditions: An Entropy Study

Bajić

Škorić

Milutinović-Smiljanić

et al. 2019

Entropy

View full text Add to dashboard Cite

show abstract

“…The entropy application proposed in this paper is motivated by a mathematical method based on copula theory [ 34 ]. The approach decomposes a multivariate joint distribution of D > 1 signals, each with an arbitrary distribution, into D independent uniform marginals and a function that binds them all—the copulas.…”

Section: Introductionmentioning

confidence: 99%

On Entropy of Probability Integral Transformed Time Series

Bajić

Mišić²,

Škorić

et al. 2020

Entropy

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The goal of this paper is to investigate the changes of entropy estimates when the amplitude distribution of the time series is equalized using the probability integral transformation. The data we analyzed were with known properties—pseudo-random signals with known distributions, mutually coupled using statistical or deterministic methods that include generators of statistically dependent distributions, linear and non-linear transforms, and deterministic chaos. The signal pairs were coupled using a correlation coefficient ranging from zero to one. The dependence of the signal samples is achieved by moving average filter and non-linear equations. The applied coupling methods are checked using statistical tests for correlation. The changes in signal regularity are checked by a multifractal spectrum. The probability integral transformation is then applied to cardiovascular time series—systolic blood pressure and pulse interval—acquired from the laboratory animals and represented the results of entropy estimations. We derived an expression for the reference value of entropy in the probability integral transformed signals. We also experimentally evaluated the reliability of entropy estimates concerning the matching probabilities.

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“…In addition to the method for the quantification of baroreflex sensitivity [4,5], classical contributions rely on power spectrum density approach, as well as on multivariate autoregressive analysis [6]. More recent analyses implement tools designed specifically to assess the mutual orderliness of simultaneously recorded time series and the level of their (a)synchrony, such as cross-entropy [7,8] and its variations such as [9], [10], [3], including the multi-scale cross-entropy [11,12,13,14,16].…”

Section: Introductionmentioning

confidence: 99%

Sequences and antisequences in hypertensive patients under therapyPlatform

Tomić¹,

Marković²,

Škorić³

et al. 2019

IJEEC

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Baroreceptor reflex (baroreflex, BRR) is a domineering physiological regulator considering the systolic blood pressure (SBP) and heart rate (HR). It maintains the negative feedback equilibrium: if the blood pressure increase, heart rate decreases and vice versa. The aim of this study is to compare the number of baroreflex sequences in hypertensive patients before and after the drug administration, and to oppose the assumptions about their origin. Other methods that evaluate the mutual connection between the SBP and HR time series are investigated as well, such as cross-entropy, copula parameter, and probability integral transformed entropy. Surrogate data were used as a control.

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Copula as a dynamic measure of cardiovascular signal interactions

Cited by 16 publications

References 30 publications

Voronoi Decomposition of Cardiovascular Dependency Structures in Different Ambient Conditions: An Entropy Study

Voronoi Decomposition of Cardiovascular Dependency Structures in Different Ambient Conditions: An Entropy Study

On Entropy of Probability Integral Transformed Time Series

Sequences and antisequences in hypertensive patients under therapyPlatform

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