Multifractal and nonlinear assessment of autonomous nervous system response during transient myocardial ischaemia

Magrans, Rudys; Gomis, Pedro; Caminal, P.; Wagner, Galen S.

doi:10.1088/0967-3334/31/4/008

Cited by 20 publications

(12 citation statements)

References 48 publications

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“…According to previous works, a reduction in the complexity of physiological control systems occurs with age and disease (Goldberger, et al 2002, Rabinovich & Abarbanel 1998, Magrans, et al 2010. Fractal power-law index β decreased significantly in symptomatic patients after exertion and recovery, when parasympathetic activity is predominant.…”

Section: Discussionmentioning

confidence: 82%

Heart rate complexity analysis in Brugada syndrome during physical stress testing

et al. 2017

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Abstract. Symptoms such as ventricular arrhythmias in Brugada syndrome (BS) typically occur at rest, especially during sleep, suggesting that the autonomic nervous system (ANS) function may be relevant in the arrhythmogenesis of the disease. The aim of this work was to assess the ANS response captured by a non-linear heart rate variability (HRV) measure in 69 patients diagnosed with BS, who underwent a standardized physical stress test. Heart rate complexity (HRC) was evaluated by the power-law scaling analysis (β slope) during rest, exercise, recovery and rest post-recovery, in order to discriminate between symptomatic and asymptomatic BS patients. Symptomatic patients showed a significant reduction in HRC in comparison to asymptomatic subjects, after exertion (p=0.015); during the whole recovery period (p=0.023), and, in particular within the passive recovery phase (p=0.025), as well as during rest post-recovery (p=0.022). Based on these results, symptoms could be associated with a lower ANS complexity during the stress test stages where parasympathetic activity is predominant. Therefore, the proposed HRV indicators could be of help in the risk stratification of asymptomatic patients.

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Section: Discussionmentioning

confidence: 82%

Heart rate complexity analysis in Brugada syndrome during physical stress testing

et al. 2017

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“…The multifractal detrended fluctuation analysis (MF-DFA) method, developed by Kantelhardt et al [22], is the multifractal extension of the detrended fluctuation analysis (DFA) method [21]. The MF-DFA method is a fairly robust and powerful tool for identifying multifractal characterization of non-stationary time series, and has been widely applied to different fields, such as biology [27], geology [28], economics [29][30][31], meteorology [14][15][16][17][18][19][20]. The MF-DFA algorithm can be briefly described as follows.…”

Section: Mf-dfa Methodsmentioning

confidence: 99%

The Scaling Properties of High-frequency Wind Speed Records Based on Multiscale Multifractal Analysis

Zeng¹,

Zhang²,

Li³

et al. 2016

Acta Phys. Pol. B

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In this paper, we employ the multiscale multifractal analysis (MMA) method to investigate the fractal properties of wind speed records depending on their magnitude of the fluctuations and the timescale. The MMA results show that the high-frequency wind speed records appear to be far more complex and contain abundant information, which cannot be detected by the popular scaling analysis method, i.e., multifractal detrended fluctuation analysis (MF-DFA). Comparing the Hurst surfaces of nine groups of wind speed data, we find that for the negative qs, all the surfaces exhibit intensive fluctuations and significant differences. In addition, the distribution histograms of Hurst surfaces for the positive qs reveal that the large fluctuations of all wind speed data depend on the spatial positions, which is further illustrated by the wind roses. Subsequent analysis of shuffled and surrogate series reveals that the multifractality of wind speed time series is mainly stemming from the long-range correlation, while has less to do with broad probability density function. Finally, the effect of sampling period is discussed. The results suggest that a sampling period of 20 min is sufficient to characterize multiscale multifractal properties of high-frequency wind speed data.

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“…In the past few decades, fractal and multifractal have been validated to be effective methods for dealing with complex problems [27,28]. For example, the multifractal theory has been widely studied and applied in fields such as economics [29], geography [30] and medicine [31]. The definition of fractal and multifractal can be inferred from the self-affine process [28].…”

Section: The Multifractal Theorymentioning

confidence: 99%

Multifractal based return interval approach for short‐term electricity price volatility risk estimation

Liu

Chung

Wen

2014

IET Generation, Transmission & Distribution

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With the ever-increasing penetration level of renewable energy generation in a power system, more uncertainties are introduced and hence risk management in the electricity market associated is becoming a more difficult issue for a market participant in the context of optimising his/her portfolio. Among a lot of risk factors in the competitive electricity market environment, the highly volatile electricity price contributes most to the financial risk of the power portfolio, especially in a short-term risk management scenario such as the spot market and real-time balancing market. Some research work has shown that the fluctuations of electricity prices exhibit multifractal characteristics, but less work has been done on the price volatility risk evaluation based on the multifractal theory. This study hence examines the feasibility of applying the multifractal theory to analyse the electricity price fluctuation, and applies the multifractal theory for evaluating the financial risk caused by electricity price volatility. A modified return interval approach considering the parameters of multifractal characteristics is employed to estimate the value-at-risk (VaR) of the electricity price. The fluctuant electricity price data series in the Pennsylvania-New Jersey-Maryland energy market are employed to demonstrate the effectiveness of the proposed VaR estimation method for short-term electricity price volatility risk evaluation.

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Multifractal and nonlinear assessment of autonomous nervous system response during transient myocardial ischaemia

Cited by 20 publications

References 48 publications

Heart rate complexity analysis in Brugada syndrome during physical stress testing

Heart rate complexity analysis in Brugada syndrome during physical stress testing

The Scaling Properties of High-frequency Wind Speed Records Based on Multiscale Multifractal Analysis

Multifractal based return interval approach for short‐term electricity price volatility risk estimation

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