Nonlinear analysis of continuous ECG during sleep I. Reconstruction

Fell, Juergen; Mann, Klaus; Röschke, Joachim; Gopinathan, M. S.

doi:10.1007/s004220050600

Cited by 37 publications

(18 citation statements)

References 24 publications

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“…The obtained values are all in the range of embedding dimension reported by others studies for cardiac signals [13]. Even m changed slightly, it remains globally stable, which implies that the model is robust and stable.…”

Section: Embedding Dimension Msupporting

confidence: 56%

Phase space reconstruction of an experimental model of cardiac field potential in normal and arrhythmic conditions

Xu¹,

Jacquir²,

Laurent³

et al. 2013

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Cardiac arrhythmias are one of the most important death causes in the world. Compared to the numerical models, the experimental ones provide a more realistic tool to study the mechanisms of cardiac arrhythmias. The in vitro culture of cardiac cells developed on the Multi-Electrodes Array (MEA) constitutes a suitable model in this context. The extracellular field potential (EFP) acquired from the MEA can be used to measure the electrophysiological parameters of action potential. In this article, the stability of this experimental model is investigated using the phase space reconstruction in normal and in arrhythmia conditions. The results show that the embedding dimension of signal EFP changed slightly in both cases (normal conditions and arrhythmia). The parameter time lag τ in the normal conditions is lower than in the arrhythmia. The shape of attractors remains similar but disturbed in case of arrhythmia compared to the normal conditions.

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Section: Embedding Dimension Msupporting

confidence: 56%

Phase space reconstruction of an experimental model of cardiac field potential in normal and arrhythmic conditions

Xu¹,

Jacquir²,

Laurent³

et al. 2013

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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“…The choice of the embedding dimension was based on the results of part I of this project (Fell et al 2000), where we determined the optimal embedding dimension for continuous ECG signals by analysing the occurrence of false nearest neighbours, as well as the saturation (convergence) of correlation dimension estimates. Results of this study allowed the conclusion that a dimension of 8 appears to be sucient to reconstruct continuous ECG signals properly.…”

Section: Methods Of Analysismentioning

confidence: 99%

Nonlinear analysis of continuous ECG during sleep II. Dynamical measures

Fell

Mann

Röschke

et al. 2000

Biological Cybernetics

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The hypothesis that cardiac rhythms are associated with chaotic dynamics implicating a healthy flexibility has motivated the investigation of continuous ECG with methods of nonlinear system theory. Sleep is known to be associated with modulations of the sympathetic and parasympathetic control of cardiac dynamics. Thus, the differentiation of ECG signals recorded during different sleep stages can serve to determine the usefulness of nonlinear measures in discriminating ECG states in general. For this purpose the following six nonlinear measures were implemented: correlation dimension D2, Lyapunov exponent L1. Kolmogorov entropy K2, as well as three measures derived from the analysis of unstable periodic orbits. Results of this study show that continuous ECG signals can be differentiated from linear stochastic surrogates by each of the nonlinear measures. The most significant finding with respect to the sleep-related differentiation of ECG signals is an increase in dominant chaoticity assessed by L1 and a reduction in the degrees of freedom estimated by D2 during REM sleep compared to slow wave sleep. Our findings suggest that the increase in dominant chaoticity during REM sleep with regard to time-continuous nonlinear analysis is comparable to an increased heart rate variability. The reduction in the correlation dimension may be interpreted as an expression of the withdrawal of respiratory influences during REM sleep.

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“…Fell et al [10] and Radhakrishna Rao et al [34] have tried the non-linear analysis of ECG and HRV signals, respectively. Several methods have been proposed: correlation dimension (CD) [35] and Poincare plot geometry [31].…”

Section: Heart Rate Variabilitymentioning

confidence: 99%