Analysing complex excitation patterns in cardiac tissue using wave event networks.

Area of Science:

• Cardiology
• Computational Biology
• Biophysics

Background:

• Cardiac dynamics involve complex electrical wave patterns, crucial for heart function.
• Disruptions in these patterns can cause arrhythmias like atrial or ventricular fibrillation.
• Optical mapping visualizes cardiac electrical waves but manual analysis is challenging.

Purpose of the Study:

• To develop and validate a novel wave tracking algorithm for analyzing cardiac electrical wave dynamics.
• To quantify complex wave patterns and identify key events like wave emergence, splitting, and merging.
• To provide a robust computational tool for systematic analysis of cardiac wave phenomena.

Main Methods:

• A graph-based wave tracking algorithm was developed to represent wave dynamics.
• The algorithm was applied to simulated cardiac tissue and experimental optical mapping data.
• Key wave events were detected and quantified, forming 'wave event networks'.

Main Results:

• The algorithm successfully identified and quantified wave patterns in both simulated and experimental data.
• Wave event networks were constructed, representing the complex dynamics of cardiac electrical waves.
• The approach demonstrated utility in filtering and focusing on dominant cardiac dynamics.

Conclusions:

• The developed wave tracking algorithm offers a robust method for analyzing cardiac wave patterns.
• This computational tool enhances the systematic quantification of cardiac electrical dynamics.
• Potential applications include studying external stimuli effects and understanding arrhythmia mechanisms.