Scale-specific and scale-independent measures of heart rate variability as risk indicators

Ashkenazy, Yosef; Lewkowicz, M.; Levitan, Jacob; Havlin, Shlomo; Saermark, K.; Moêlgaard, H.; Thomsen, Poul Erik Bloch; Møller, Mogens; Hintze, Ulrik; Huikuri, Heikki V.

doi:10.1209/epl/i2001-00208-x

Cited by 33 publications

(12 citation statements)

References 23 publications

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“…Upon employing two recent wellestablished methods, i.e., Detrended Fluctuation Analysis [1][2][3] and Multiresolution Wavelet Analysis (see ref. [4] and references therein), the following results have been obtained [5]: First, the variance and the scaling exponent are uncorrelated. Second, the variance measure at certain scales is well suited to separate H from heart patients.…”

mentioning

confidence: 92%

Scale-specific order parameter fluctuations of seismicity in natural time before mainshocks

Varotsos

Sarlis

Skordas

2011

EPL

106

101

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We have previously shown that the probability distribution of the order parameter κ1 of seismicity in natural time turns to be bimodal when approaching a mainshock. This reflects that, for various natural time window lengths ending at a given mainshock, the fluctuations of κ1 considerably increase for smaller lengths, i.e., upon approaching a mainshock. Here, as a second step, we investigate the order parameter fluctuations, but when considering a natural time window of a fixed-length sliding through a seismic catalog. We find that when this length becomes comparable with the lead time of Seismic Electric Signals activities (i.e., of the order of a few months), the fluctuations exhibit a global minimum before the strongest mainshock. Thus, the approach of the latter is characterized by two distinct features of the order parameter fluctuations that complement each other.

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confidence: 92%

Scale-specific order parameter fluctuations of seismicity in natural time before mainshocks

Varotsos

Sarlis

Skordas

2011

EPL

106

101

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“…Biological systems usually consist of several subsystems which are interrelated by feedbacks with time delay. To reveal such time-delayed coupling directions from biosignals is a basic task in understanding such systems [1][2][3][4][5][6][7][8]. Data recorded from these systems reflect biological activities of living beings and are characterized on the one hand by real biological information, including nonstationarities, nonlinearities and intrinsic noise, and on the other hand by measurement noise.…”

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confidence: 99%

Detection of time-delayed interactions in biosignals using symbolic coupling traces

et al. 2009

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Directional coupling analysis of bivariate time series is an important subject of current research. In this letter, a method based on symbolic dynamics for the detection of time-delayed coupling is presented. The symbolic coupling traces, defined as the symmetric and diametric traces of the bivariate word distribution, allow for the quantification of coupling and are compared with established methods like mutual information and cross recurrence analysis. The symbolic coupling traces method is applied to model systems and cardiological data which demonstrate its advantages especially for nonstationary data.

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“…[28] and references therein). It has been shown [34] that scale-specific and scale-independent measures are uncorrelated and that the former (scale-specific) perform better as diagnostic tools whereas the latter (scale-independent) as prediction tools. Moreover, since physiological signals may contain both stochastic and deterministic components, the concept of entropy is also suitable for the study of ECG.…”

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confidence: 99%

Change ΔS of the entropy in natural time under time reversal: Complexity measures upon change of scale

Sarlis¹,

Christopoulos²,

Bemplidaki³

2015

EPL

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-The entropy S in natural time as well as the entropy in natural time under time reversal S− have already found useful applications in the physics of complex systems, e.g., in the analysis of electrocardiograms (ECGs). Here, we focus on the complexity measures Λ l which result upon considering how the statistics of the time series ∆S [≡ S − S−] changes upon varying the scale l. These scale specific measures are ratios of the standard deviations σ(∆S l ) and hence independent of the mean value and the standard deviation of the data. They focus on the different dynamics that appear on different scales. For this reason, they can be considered complementary to other standard measures of heart rate variability in ECG, like SDNN, as well as other complexity measures already defined in natural time. An application to the analysis of ECG -when solely using NN intervals-is presented: We show how Λ l can be used to separate ECG of healthy individuals from those suffering from congestive heart failure and sudden cardiac death.Introduction. -Natural time analysis [1][2][3] focuses in the sequential order of events occurring in a complex system. It extracts the maximum information possible from a given time series [4]. If we consider a series of N events in a complex system, the natural time attributed to the k-th event is given by χ k = k/N . For example, as shown in fig.1, in the case of an electrocardiogram (ECG) as events we may consider the heartbeats. In natural time analysis, χ k is complemented by a quantity Q k which is proportional to the energy emitted during the k-th event. Further analysis is made by studying the pair (χ k , Q k ) and introducing the normalized energy. . N ) sum up to unity, they can be considered as probabilities corresponding to χ k . The entropy S in natural time is defined [1,5,6] by S = ⟨χ ln χ⟩ − ⟨χ⟩ ln⟨χ⟩,

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Scale-specific and scale-independent measures of heart rate variability as risk indicators

Cited by 33 publications

References 23 publications

Scale-specific order parameter fluctuations of seismicity in natural time before mainshocks

Scale-specific order parameter fluctuations of seismicity in natural time before mainshocks

Detection of time-delayed interactions in biosignals using symbolic coupling traces

Change ΔS of the entropy in natural time under time reversal: Complexity measures upon change of scale

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