Atrial fibrillation driver identification through regional mutual information networks: a modeling perspective

Abstract

Purpose Effective identification of electrical drivers within remodeled tissue is a key for improving ablation treatment for atrial fibrillation. We have developed a mutual information, graph-based approach to identify and propose fault tolerance metric of local efficiency as a distinguishing feature of rotational activation and remodeled atrial tissue. Methods Voltage data were extracted from atrial tissue simulations (2D Karma, 3D physiological, and the Multiscale Cardiac Simulation Framework (MSCSF)) using multi-spline open and parallel regional mapping catheter geometries. Graphs were generated based on varied mutual information thresholds between electrode pairs and the local efficiency for each graph was calculated. Results High-resolution mapping catheter geometries can distinguish between rotational and irregular activation patterns using the derivative of local efficiency as a function of increasing mutual information threshold. The derivative is decreased for rotational activation patterns comparing to irregular activations in both a simplified 2D model (0.0017 ± 1 × 10−4 vs. 0.0032 ± 1 × 10−4, p < 0.01) and a more realistic 3D model (0.00092 ± 5 × 10−5 vs. 0.0014 ± 4 × 10−5, p < 0.01). Average local efficiency derivative can also distinguish between degrees of remodeling. Simulations using the MSCSF model, with 10 vs. 90% remodeling, display distinct derivatives in the grid design parallel spline catheter configuration (0.0015 ± 5 × 10−5 vs. 0.0019 ± 6 × 10−5, p < 0.01) and the flower shaped open spline configuration (0.0011 ± 5 × 10−5 vs. 0.0016 ± 4 × 10−5, p < 0.01). Conclusion A decreased derivative of local efficiency characterizes rotational activation and varies with atrial remodeling. This suggests a distinct communication pattern in cardiac rotational activation detectable via high-resolution regional mapping and could enable identification of electrical drivers for targeted ablation.

How synergy between mechanistic and statistical models is impacting research in atrial fibrillation

“…Another application is to identify potential ablation targets. In these studies, mechanistic models were used to simulate the typical AF scenarios and statistical models were used to find the regions in the atria where arrhythmias are inducible ( Sha et al, 2022 ). For example, Ravikumar et al evaluated the performance of multiscale frequency [MSF], Shannon entropy [SE], kurtosis [Kt], and multiscale entropy [MSE] techniques to identify the pivot point of the rotor using unipolar and bipolar EGMs obtained from numerical simulations ( Ravikumar et al, 2021 ).…”