A three-dimensional human atrial model with fiber orientation. Electrograms and arrhythmic activation patterns...

Area of Science:

• Cardiology
• Computational Biology
• Biophysics

Background:

• Atrial tachyarrhythmias, particularly atrial fibrillation, are common cardiac arrhythmias.
• Complex fractionated atrial electrograms and high dominant frequencies are implicated in maintaining atrial fibrillation.
• A gap exists in understanding the link between electrogram characteristics and underlying propagation patterns.

Purpose of the Study:

• To investigate the relationship between atrial electrogram characteristics and underlying propagation patterns using a realistic 3D human atrial model.
• To simulate various tachyarrhythmic episodes and analyze electrogram properties.

Main Methods:

• Developed a realistic 3D computational model of the human atria, incorporating realistic geometry, fiber orientation, anisotropic conductivity, and electrophysiological heterogeneity.
• Simulated transient and continuous ectopic activity to induce tachyarrhythmic episodes.
• Calculated electrogram dominant frequency and organization index values across the atrial surface.

Main Results:

• Stable, regular activity (atrial flutter, tachycardia, focal atrial fibrillation) correlated with simple electrograms, narrow frequency peaks, and high organization index.
• Irregular, unstable atrial fibrillation activity showed polymorphic electrograms with variable cycle lengths and fragmented potentials.
• Specific electrogram features were linked to distinct propagation phenomena: simple potentials to wavefronts moving away, positive deflections to wave collisions, double potentials to wave fragmentation/block lines, and fragmented electrograms to pivot points.

Conclusions:

• The study successfully modeled the relationship between atrial electrogram characteristics and underlying wave propagation patterns in various arrhythmias.
• The findings provide a deeper understanding of how different electrogram morphologies relate to specific mechanisms of atrial tachyarrhythmias.
• This computational approach offers a novel tool for investigating cardiac electrophysiology and arrhythmia mechanisms.