Asymmetric detrended fluctuation analysis in neonatal stress

Šapina, Matej; Kośmider, Marcin; Kramarić, Karolina; Garcin, Matthieu; Adelson, P. David; Pirić, Marko; Milas, Krešimir; Brdarić, Dario

doi:10.1088/1361-6579/aad425

Cited by 8 publications

(8 citation statements)

References 43 publications

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“…Our model has thus two components: a fractal dynamic and a mean-reversion, which account for high-and low-frequency HRV. This is consistent with the findings of papers estimating Hurst exponent in HRV, in which a crossover phenomenon is described, with different scaling properties in a short range and in a longer range [41,7], or with different scaling properties for accelerations and decelerations of heart rate [48]. This depicts the complexity of the HRV and of the autonomic nervous system.…”

Section: Conclusion and Discussionsupporting

confidence: 91%

“…This last observation is consistent with the fact that entropy also decreases during stress phases of neonates, which indicates a lower uncertainty in HRV [47]. It is also consistent with the increase of the Hurst exponent of decelerations in the asymmetric scaling approach, during stress phases [48]. Indeed, such an increase smooths the fluctuations and thus makes the HRV more predictable.…”

Section: Conclusion and Discussionsupporting

confidence: 86%

“…For these dynamics, the most relevant parameter is the Hurst exponent, which quantifies the time scaling of the stochastic process or time series: by definition, if we note H the Hurst exponent of a stochastic process S t , an increment of duration d, S t+d − S t , has the same probability distribution as d H (S t+1 − S t ), for all time t. The Hurst exponent can be estimated by several methods. For the analysis of the HRV, it has been estimated by the method of absolute moments [9], rescaled range analysis (R/S) [31,34] and detrended fluctuation analysis (DFA), in which the HRV signal is detrended by a piecewise linear trend [41,40,13,52,48] or by a moving average [5]. Among these three techniques, our preference goes to the method of absolute moments [39,18] because it does not need a dataset as big as for computing R/S analysis or DFA, which are based on regressions on many subsamples.…”

Section: Introductionmentioning

confidence: 99%

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The Hurst Exponent of Heart Rate Variability in Neonatal Stress, Based on a Mean-Reverting Fractional Lévy Stable Motion

et al. 2020

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We aim at detecting stress in newborns by observing heart rate variability (HRV). The HRV features nonlinearities. Fractal dynamics is a usual way to model them and the Hurst exponent summarizes the fractal information. In our framework, we have observations of short duration, for which usual estimators of the Hurst exponent, like detrended fluctuation analysis (DFA), are not adapted. Moreover, we observe that the Hurst exponent does not vary much between stress and rest phases, but its decomposition in memory and underlying properties of the probability distribution leads to satisfactory diagnostic tools. This decomposition of the Hurst exponent is in addition embedded in a mean-reverting model. The resulting model is a mean-reverting fractional Lévy stable motion (FLSM). We estimate it and use its parameters as diagnostic tools of neonatal stress. Indeed, the value of the speed of reversion parameter is a significant indicator of stress. The evolution of both parameters in which the Hurst exponent is decomposed provides us with significant indicators as well. On the contrary, the Hurst exponent itself does not bear useful information.

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Section: Conclusion and Discussionsupporting

confidence: 91%

Section: Conclusion and Discussionsupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

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The Hurst Exponent of Heart Rate Variability in Neonatal Stress, Based on a Mean-Reverting Fractional Lévy Stable Motion

et al. 2020

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“…This is consistent with previous findings, that the HRV of neonates in stress phases results in: (1) an increasing part of mean-reversion effect compared to random fluctuations; (2) an increasing negative scaling exponent, depicting a higher autocorrelation of the accelerations in the RR i series. This higher autocorrelation, acting as a fractional integral, smooths the series and thus reduces fluctuations [30,38].…”

Section: Discussionmentioning

confidence: 99%

Multi-lag tone–entropy in neonatal stress

Šapina

Karmakar

Kramarić

et al. 2018

J. R. Soc. Interface.

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Heart rate variability (HRV) has been analysed using linear and nonlinear methods. In the framework of a controlled neonatal stress model, we applied tone–entropy (T–E) analysis at multiple lags to understand the influence of external stressors on healthy term neonates. Forty term neonates were included in the study. HRV was analysed using multi-lag T–E at two resting and two stress phases (heel stimulation and a heel stick blood drawing phase). Higher mean entropy values and lower mean tone values when stressed showed a reduction in randomness with increased sympathetic and reduced parasympathetic activity. A ROC analysis was used to estimate the diagnostic performances of tone and entropy and combining both features. Comparing the resting and simulation phase separately, the performance of tone outperformed entropy, but combining the two in a quadratic linear regression model, neonates in resting as compared to stress phases could be distinguished with high accuracy. This raises the possibility that when applied across short time segments, multi-lag T–E becomes an additional tool for more objective assessment of neonatal stress.

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“…anti-persistence) of the increments. Therefore, in fields as diverse as finance [11,27,15,20] or medicine [33,34], researchers hunt for values of Hurst exponents above 1/2, in order to leverage the persistence of the series and make predictions. Or, on the contrary, values of Hurst exponents below 1/2 are expected to make us benefit from the mean reversion of the series, as in pair trading [32].…”

Section: Introductionmentioning

confidence: 99%

Hurst Exponents and Delampertized Fractional Brownian Motions

Garcin

2019

Int. J. Theor. Appl. Finan.

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The inverse Lamperti transform of a fractional Brownian motion (fBm) is a stationary process. We determine the empirical Hurst exponent of such a composite process with the help of a regression of the log absolute moments of its increments, at various scales, on the corresponding log scales. This perceived Hurst exponent underestimates the Hurst exponent of the underlying fBm. We thus encounter some time series having a perceived Hurst exponent lower than [Formula: see text], but an underlying Hurst exponent higher than [Formula: see text]. This paves the way for short- and medium-term forecasting. Indeed, in such series, mean reversion predominates at high scales, whereas persistence is overriding at lower scales. We propose a way to characterize the Hurst horizon, namely a limit scale between these opposite behaviors. We show that the delampertized fBm, which mixes persistence and mean reversion, is relevant for financial time series, in particular for high-frequency foreign exchange rates. In our sample, the empirical Hurst horizon is always above 1[Formula: see text]h and 23[Formula: see text]min.

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Asymmetric detrended fluctuation analysis in neonatal stress

Abstract: ADFA, particularly of the acceleration scaling exponent, may be a useful clinical diagnostic tool for monitoring neonatal stress.

Cited by 8 publications

References 43 publications

The Hurst Exponent of Heart Rate Variability in Neonatal Stress, Based on a Mean-Reverting Fractional Lévy Stable Motion

The Hurst Exponent of Heart Rate Variability in Neonatal Stress, Based on a Mean-Reverting Fractional Lévy Stable Motion

Multi-lag tone–entropy in neonatal stress

Hurst Exponents and Delampertized Fractional Brownian Motions

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