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Quantifying spatiotemporal complexity of cardiac dynamics using ordinal patterns

MPS-Authors
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Schlemmer,  Alexander
Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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Berg,  Sebastian
Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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Shajahan,  T. K.
Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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Luther,  Stefan
Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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Parlitz,  Ulrich
Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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Citation

Schlemmer, A., Berg, S., Shajahan, T. K., Luther, S., & Parlitz, U. (2015). Quantifying spatiotemporal complexity of cardiac dynamics using ordinal patterns. In Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE (pp. 4049-4052).


Cite as: http://hdl.handle.net/11858/00-001M-0000-0029-7BC2-A
Abstract
Analyzing the dynamics of complex excitation wave patterns in cardiac tissue plays a key role for understanding the origin of life-threatening arrhythmias and for devising novel approaches to control them. The quantification of spatiotemporal complexity, however, remains a challenging task. This holds in particular for the analysis of data from fluorescence imaging (optical mapping), which allows for the measurement of membrane potential and intracellular calcium at high spatial and temporal resolution. Hitherto methods, like dominant frequency maps and the analysis of phase singularities, address important aspects of cardiac dynamics, but they consider very specific properties of excitable media, only. This article focuses on the benchmark of spatial complexity measures over time in the context of cardiac cell cultures. Standard Shannon Entropy and Spatial Permutation Entropy, an adaption of [1], have been implemented and applied to optical mapping data from embryonic chicken cell culture experiments. We introduce spatial separation of samples when generating ordinal patterns and show its importance for Spatial Permutation Entropy. Results suggest that Spatial Permutation Entropies provide a robust and interpretable measure for detecting qualitative changes in the dynamics of this excitable medium.