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

Hans Friedrich Von Koeller^1,2, Alexander Schlemmer¹, Stefan Luther^1,2,3,4

¹Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany.

Frontiers in Network Physiology

|December 4, 2025

Summary

A new algorithm analyzes cardiac electrical waves by creating wave event networks. This tool quantifies complex wave patterns in cardiac dynamics, aiding research into arrhythmias.

Keywords:

atrial and ventricular fibrillation cardiac arrhythmias cardiac dynamics focal activity network physiology polymorphictachycardia spontaneous emissions wave tracking

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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.