Multi‐fractal detrended fluctuation half‐spectrum analysis of HRV

Zhang, Daoming; Wang, Cong; Li, Chuangye; Dai, Wei

doi:10.1049/joe.2019.1067

Cited by 8 publications

(7 citation statements)

References 22 publications

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“…is also a FP with the same statistical distributions than the y(t) process, and in which it is verified that D is given by the expression [20]…”

Section: Fractal Processesmentioning

confidence: 78%

“…In [20] they estimate D based on the power-law behavior expressed by the above expression. Moreover from the definition of fractional Brownian motion (fBm), these fBm processes must be governed by [26]   where 0< H < 1 is the Hurst exponent of the fBm process.…”

Section: Fractal Processesmentioning

confidence: 99%

“…The power-spectral function provides an important parameter to characterize persistence in time series. For a fractal time series, its power-spectral function [20] obeys the frequency-based power-law behavior and is given by the expression…”

Section: Power-spectral Analysis (Psa)mentioning

confidence: 99%

“…Several approaches have explored to calculate the fractal dimension of a subset with respect to a fractal structure. A discrete models of fractal dimension to explore the complexity of discrete dynamical systems [20].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Time Series Analysis of Computer Network Traffic in a Dedicated Link Aggregation

Millán¹,

Lefranc²,

Osorio-Comparán³

et al. 2021

Preprint

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Fractal behavior and long-range dependence are widely observed in measurements and characterization of traffic flow in high-speed computer networks of different technologies and coverage levels. This paper presents the results obtained when applying fractal analysis techniques on a time series obtained from traffic captures coming from an application server connected to the Internet through a high-speed link. The results obtained show that traffic flow in the dedicated high-speed network link have fractal behavior when the Hurst exponent is in the range of 0.5, 1, the fractal dimension between 1, 1.5, and the correlation coefficient between –0.5, 0. Based on these results, it is ideal to characterize both the singularities of the traffic and its impulsiveness during a fractal analysis of temporal scales. Finally, based on the results of the time series analyses, the fact that the traffic flows of current computer networks exhibit fractal behavior with a long-range dependency is reaffirmed.

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“…is also a FP with the same statistical distributions than the y(t) process, and in which it is verified that D is given by the expression [20]…”

Section: Fractal Processesmentioning

confidence: 78%

Section: Fractal Processesmentioning

confidence: 99%

Section: Power-spectral Analysis (Psa)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Time Series Analysis of Computer Network Traffic in a Dedicated Link Aggregation

Millán¹,

Lefranc²,

Osorio-Comparán³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Here, D q reflects the multi-fractal spectrum of the subset of series and its value reflects the fractal dimension with the singularity exponent of h q (Zhang et al, 2019) which is inversely related to singularity (i.e. bigger singularities have smaller h q values).…”

Section: Mfi(s)mentioning

confidence: 99%

Evaluation of non-linear heart rate variability using multi-scale multi-fractal detrended fluctuation analysis in mice: Roles of the autonomic nervous system and sinoatrial node

2022

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Nonlinear analyses of heart rate variability (HRV) can be used to quantify the unpredictability, fractal properties and complexity of heart rate. Fractality and its analysis provides valuable information about cardiovascular health. Multi-Scale Multi-Fractal Detrended Fluctuation Analysis (MSMFDFA) is a complexity-based algorithm that can be used to quantify the multi-fractal dynamics of the HRV time series through investigating characteristic exponents at different time scales. This method is applicable to short time series and it is robust to noise and nonstationarity. We have used MSMFDFA, which enables assessment of HRV in the frequency ranges encompassing the very-low frequency and ultra-low frequency bands, to jointly assess multi-scale and multi-fractal dynamics of HRV signals obtained from telemetric ECG recordings in wildtype mice at baseline and after autonomic nervous system (ANS) blockade, from electrograms recorded from isolated atrial preparations and from spontaneous action potential recordings in isolated sinoatrial node myocytes. Data demonstrate that the fractal profile of the intrinsic heart rate is significantly different from the baseline heart rate in vivo, and it is also altered after ANS blockade at specific scales and fractal order domains. For beating rate in isolated atrial preparations and intrinsic heart rate in vivo, the average fractal structure of the HRV increased and multi-fractality strength decreased. These data demonstrate that fractal properties of the HRV depend on both ANS activity and intrinsic sinoatrial node function and that assessing multi-fractality at different time scales is an effective approach for HRV assessment.

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Fractal and multifractal characterization of in vitro respiratory recordings of the pre-Bötzinger complex

et al. 2021

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Multi‐fractal detrended fluctuation half‐spectrum analysis of HRV

Cited by 8 publications

References 22 publications

Time Series Analysis of Computer Network Traffic in a Dedicated Link Aggregation

Time Series Analysis of Computer Network Traffic in a Dedicated Link Aggregation

Evaluation of non-linear heart rate variability using multi-scale multi-fractal detrended fluctuation analysis in mice: Roles of the autonomic nervous system and sinoatrial node

Fractal and multifractal characterization of in vitro respiratory recordings of the pre-Bötzinger complex

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