Atrial sources identification by causality analysis during atrial fibrillation

Area of Science:

• Cardiovascular Electrophysiology
• Computational Biology
• Medical Physics

Background:

• Atrial fibrillation (AF) recurrence is often driven by specific electrical sources.
• Identifying these drivers is crucial for effective catheter ablation therapy.
• Current methods for driver identification can be limited in complex AF dynamics.

Purpose of the Study:

• To develop and validate a novel causality analysis methodology for identifying dominant atrial drivers of AF.
• To assess the ability of this method to map hierarchical driver dominance and location.
• To evaluate the potential of this technique to guide AF ablation strategies.

Main Methods:

• Utilized realistic mathematical models of atrial electrical activity during AF.
• Applied causality analysis to compute causal relations between multiple electrogram signals.
• Summarized causal relationships into a recurrence map to visualize driver hierarchy and dominance.

Main Results:

• The causality analysis successfully identified dominant atrial propagation patterns and driver locations in simulated AF.
• Recurrence maps accurately highlighted hierarchical driver dominance, even with single, dual, or multiple rotors.
• A correlation was established between recurrence map nodal values and proximity to the rotor core.

Conclusions:

• Causality analysis provides a robust method for estimating AF propagation patterns and locating critical drivers.
• The developed recurrence maps can effectively identify arrhythmogenic sites responsible for AF maintenance.
• This methodology shows promise for guiding and improving the efficacy of AF ablation procedures.