Discrimination Between Healthy and Sick Cardiac Autonomic Nervous System by Detrended Heart Rate Variability Analysis

Ashkenazy, Yosef; Lewkowicz, M.; Levitan, Jacob; Havlin, Shlomo; Saermark, K.; Moêlgaard, H.; Thomsen, Poul Erik Bloch

doi:10.1142/s0218348x99000104

Cited by 31 publications

(25 citation statements)

References 14 publications

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“…Recently, the method of DFA [27] has shown its potential in revealing monofractal scaling behavior and detecting long-range correlations in noisy and nonstationary time series [11,28] in many different scientific fields (genetics [27,29], cardiology [30], geology [31], economics [32]). …”

Section: Discussionmentioning

confidence: 99%

Multifractal fluctuations in seismic interspike series

Telesca

Lapenna

Macchiato

2005

Physica A: Statistical Mechanics and its Applications

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Multifractal fluctuations in the time dynamics of seismicity data have been analyzed. We investigated the interspike intervals (times between successive earthquakes) of one of the most seismically active areas of central Italy by using the Multifractal Detrended Fluctuation Analysis (MF-DFA). Analyzing the time evolution of the multifractality degree of the series, a loss of multifractality during the aftershocks is revealed. This study aims to suggest another approach to investigate the complex dynamics of earthquakes. 05.45.Df, 05.45.Tp, 24.60.Ky PACS number(s):

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Section: Discussionmentioning

confidence: 99%

Multifractal fluctuations in seismic interspike series

Telesca

Lapenna

Macchiato

2005

Physica A: Statistical Mechanics and its Applications

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“…(14). In addition, n 1× is shifted to larger scales when higher order DFA-ℓ is applied, due to the fact that the value of F S (n) decreases when ℓ increases (α S = ℓ + 1, see Fig.…”

Section: ×mentioning

confidence: 98%

“…Furthermore, the DFA method may help identify different states of the same system according to its different scaling behaviors -e.g. the scaling exponent α for heart inter-beat intervals is different for healthy and sick individuals [14,16,17,53].…”

Section: Introductionmentioning

confidence: 99%

“…The advantages of DFA over many methods are that it permits the detection of long-range correlations embedded in seemingly non-stationary time series, and also avoids the spurious detection of apparent long-range correlations that are artifact of non-stationarity. In the past few years, more than 100 publications have utilized the DFA as method of correlation analysis, and have uncovered long-range power-law correlations in many research fields such as cardiac dynamics [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23], bioinformatics [1,2,[24][25][26][27][28][29][30][31][32][33][34], economics [35][36][37][38][39][40][41][42][43][44][45][46][47], meteorology [48][49]…”

Section: Introductionmentioning

confidence: 99%

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Effect of trends on detrended fluctuation analysis

et al. 2001

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Detrended fluctuation analysis (DFA) is a scaling analysis method used to estimate long-range power-law correlation exponents in noisy signals. Many noisy signals in real systems display trends, so that the scaling results obtained from the DFA method become difficult to analyze. We systematically study the effects of three types of trends -linear, periodic, and power-law trends, and offer examples where these trends are likely to occur in real data. We compare the difference between the scaling results for artificially generated correlated noise and correlated noise with a trend, and study how trends lead to the appearance of crossovers in the scaling behavior. We find that crossovers result from the competition between the scaling of the noise and the "apparent" scaling of the trend. We study how the characteristics of these crossovers depend on (i) the slope of the linear trend; (ii) the amplitude and period of the periodic trend; (iii) the amplitude and power of the power-law trend and (iv) the length as well as the correlation properties of the noise. Surprisingly, we find that the crossovers in the scaling of noisy signals with trends also follow scaling laws -i.e. long-range power-law dependence of the position of the crossover on the parameters of the trends. We show that the DFA result of noise with a trend can be exactly determined by the superposition of the separate results of the DFA on the noise and on the trend, assuming that the noise and the trend are not correlated. If this superposition rule is not followed, this is an indication that the noise and the superimposed trend are not independent, so that removing the trend could lead to changes in the correlation properties of the noise. In addition, we show how to use DFA appropriately to minimize the effects of trends, and how to recognize if a crossover indicates indeed a transition from one type to a different type of underlying correlation, or the crossover is due to a trend without any transition in the dynamical properties of the noise.

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“…The DFA method has been successfully applied to research fields such as DNA [3,[5][6][7][8][9][10][11][12][13][14][15][16], cardiac dynamics , human gait [38], meteorology [39], climate temperature fluctuations [40][41][42], river flow and discharge [43,44], neural receptors in biological systems [45], and economics [46][47][48][49][50][51][52][53][54][55][56][57][58]. The DFA method may also help identify different states of the same system with different scaling behavior -e.g., the scaling exponent α for heart-beat intervals is different for healthy and sick individuals [17,28] as well as for waking and sleeping states [23,33].…”

Section: Introductionmentioning

confidence: 99%

Effect of nonstationarities on detrended fluctuation analysis

et al. 2002

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Detrended fluctuation analysis (DFA) is a scaling analysis method used to quantify long-range power-law correlations in signals. Many physical and biological signals are "noisy", heterogeneous and exhibit different types of nonstationarities, which can affect the correlation properties of these signals. We systematically study the effects of three types of nonstationarities often encountered in real data. Specifically, we consider nonstationary sequences formed in three ways: (i) stitching together segments of data obtained from discontinuous experimental recordings, or removing some noisy and unreliable parts from continuous recordings and stitching together the remaining parts -a "cutting" procedure commonly used in preparing data prior to signal analysis; (ii) adding to a signal with known correlations a tunable concentration of random outliers or spikes with different amplitude, and (iii) generating a signal comprised of segments with different properties -e.g. different standard deviations or different correlation exponents. We compare the difference between the scaling results obtained for stationary correlated signals and correlated signals with these three types of nonstationarities. We find that introducing nonstationarities to stationary correlated signals leads to the appearance of crossovers in the scaling behavior and we study how the characteristics of these crossovers depend on: (a) the fraction and size of the parts cut out from the signal; (b) the concentration of spikes and their amplitudes; (c) the proportion between segments with different standard deviations or different correlations; and (d) the correlation properties of the stationary signal. We show how to develop strategies for pre-processing "raw" data prior to analysis, which will minimize the effects of nonstationarities on the scaling properties of the data and how to interpret the results of DFA for complex signals with different local characteristics.

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Discrimination Between Healthy and Sick Cardiac Autonomic Nervous System by Detrended Heart Rate Variability Analysis

Cited by 31 publications

References 14 publications

Multifractal fluctuations in seismic interspike series

Multifractal fluctuations in seismic interspike series

Effect of trends on detrended fluctuation analysis

Effect of nonstationarities on detrended fluctuation analysis

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