Quantifying spatiotemporal complexity of cardiac dynamics using ordinal patterns.

Area of Science:

• Physiology
• Biophysics
• Complex Systems

Background:

• Understanding cardiac arrhythmias requires analyzing complex excitation wave patterns in cardiac tissue.
• Quantifying spatiotemporal complexity in optical mapping data (measuring membrane potential and intracellular calcium) is challenging.
• Existing methods like dominant frequency maps and phase singularity analysis capture only specific aspects of cardiac dynamics.

Purpose of the Study:

• To benchmark spatial complexity measures over time for cardiac cell cultures.
• To evaluate the applicability of Shannon Entropy and Spatial Permutation Entropy to optical mapping data.
• To introduce and assess the importance of spatial separation in generating ordinal patterns for Spatial Permutation Entropy.

Main Methods:

• Implementation and application of standard Shannon Entropy.
• Adaptation and application of Spatial Permutation Entropy, including a novel spatial separation method for ordinal pattern generation.
• Analysis of optical mapping data from embryonic chicken cell culture experiments.

Main Results:

• Spatial Permutation Entropy, particularly with spatial separation, proves effective for analyzing cardiac cell culture dynamics.
• The method provides a robust and interpretable measure for detecting qualitative changes in excitable media.
• Comparison highlights the advantages of Spatial Permutation Entropy over traditional methods for capturing complex dynamics.

Conclusions:

• Spatial Permutation Entropy is a valuable tool for quantifying spatiotemporal complexity in cardiac dynamics.
• The developed method enhances the analysis of optical mapping data, aiding arrhythmia research.
• This approach offers a more comprehensive understanding of wave propagation and pattern formation in excitable media.