Complexity analysis of experimental cardiac arrhythmia

To study the cardiac arrhythmia, an in vitro experimental model and Multielectrodes Array (MEA) are used. This platform serves as an intermediary of the electrical activities of cardiac cells and the signal processing / dynamics analysis. Through it the extracellular potential of cardiac cells is acquired, allowing a real-time monitoring / analyzing. Since MEA has 60 electrodes / channels dispatched in a rectangular region, it allows real-time monitoring and signal acquisition on multiple sites. The in vitro experimental model (cardiomyocytes cultures from newborn rats'heart) is directly prepared on the MEA. This carefully prepared culture has similar parameters as cell of human's heart. In order to discriminate the cardiac arrhythmia, complexity analysis methods (Approximate Entropy, ApEn and Sample Entropy, SampEn) are used especially taking into account noise. The results showed that, in case of arrhythmia, the ApEn and SampEn are reduced to about 50% of the original entropies. Both parameters could be used as factors to discriminate arrhythmia. Moreover, from a point of view of biophysics this decrease 50% of Entropy coincides with the bifurcation (periods, attractors etc.) in case of arrhythmia which have been reported previously. It supports once more the hypothesis that in case of cardiac arrhythmia, the heart entered into chaos which helps to better understand the mechanism of atrial fibrillation.

[1]  S M Pincus,et al.  Approximate entropy as a measure of system complexity. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[2]  P Athias,et al.  [Development of cardiac physiopathological models from cultured cardiomyocytes]. , 2006, Annales de cardiologie et d'angeiologie.

[3]  Anatoly A. Zhigljavsky,et al.  Analysis of Time Series Structure - SSA and Related Techniques , 2001, Monographs on statistics and applied probability.

[4]  Brij N. Singh,et al.  Optimal selection of wavelet basis function applied to ECG signal denoising , 2006, Digit. Signal Process..

[5]  José Jalife,et al.  Cardiac electrophysiology and arrhythmias , 1985 .

[6]  H. Abarbanel,et al.  Determining embedding dimension for phase-space reconstruction using a geometrical construction. , 1992, Physical review. A, Atomic, molecular, and optical physics.

[7]  F. Takens.  Detecting strange attractors in turbulence , 1981 .

[8]  Andreas Möller,et al.  On Micro-Electrode Array Revival: Its Development, Sophistication of Recording, and Stimulation , 2006 .

[9]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[10]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .

[11]  Schwartz,et al.  Singular-value decomposition and the Grassberger-Procaccia algorithm. , 1988, Physical review. A, General physics.

[12]  Binbin Xu,et al.  Phase space reconstruction of an experimental model of cardiac field potential in normal and arrhythmic conditions , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[13]  Pierre Athias,et al.  Développement de modèles physiopathologiques cardiaques à partir des cultures de cardiomyocytes , 2006 .

[14]  Stéphane Binczak,et al.  Detection of Complex Fractionated Atrial Electrograms Using Recurrence Quantification Analysis , 2013, IEEE Transactions on Biomedical Engineering.

[15]  S. Jacquir,et al.  Cardiac arrhythmia induced by hypothermia in a cardiac model in vitro , 2013 .

[16]  I. Johnstone,et al.  Ideal spatial adaptation by wavelet shrinkage , 1994 .

[17]  S. Nattel,et al.  Vagal Atrial Fibrillation , 2007 .

[18]  D. Ruelle,et al.  Recurrence Plots of Dynamical Systems , 1987 .

[19]  Desire L. Massart,et al.  Optimization of signal denoising in discrete wavelet transform , 1999 .

[20]  P. Athias,et al.  Myocardial electrophysiology: intracellular studies on heart cell cultures from newborn rats. , 1979, Pathologie-biologie.

[21]  Binbin Xu,et al.  Investigation of Micro Spiral Waves at Cellular Level Using a Microelectrode Arrays Technology , 2011, Int. J. Bifurc. Chaos.

[22]  M. Allessie,et al.  Experimental evaluation of Moe's multiple wavelet hypothesis of atrial fibrillation , 1985 .

[23]  Konrad Brockmeier,et al.  Microelectrode arrays: a new tool to measure embryonic heart activity. , 2004, Journal of electrocardiology.

[24]  Micha E. Spira,et al.  Toward on-chip, in-cell recordings from cultured cardiomyocytes by arrays of gold mushroom-shaped microelectrodes , 2012, Front. Neuroeng..