Directed network mapping approach for rotor localization in atrial fibrillation simulation
This article was originally published here
Annu Int Conf IEEE Eng Med Biol Soc. November 2021; 2021: 730-733. doi: 10.1109 / EMBC46164.2021.9629911.
Catheter ablation for atrial fibrillation (AF) is one of the most commonly performed electrophysiology procedures. Despite significant advances in our understanding of the mechanisms of AF in recent years, ablation results remain suboptimal for many patients, particularly those with persistent or long-lasting AF. One possible reason is that ablation techniques focus primarily on anatomical, rather than patient-specific, functional targets for ablation. The identification of such ablation targets remains difficult. The aim of this study is to investigate a novel approach based on directed networks, which allow the automatic detection of important arrhythmia mechanisms, which may be practical to guide the ablation strategy. Arrays are generated by processing unipolar electrograms (EGMs) collected by catheters positioned in different regions of the atria. The vertices of the array represent the locations of the records, and the outlines are determined using a cross-covariance delay estimation method. The algorithm identifies spinning activity, propagating from top to top creating a cycle. This work is a simulation study and it uses a very detailed 3D computer model of the human atria in which sustained activation of the atrial rotor was performed. Virtual electrodes were placed on the endocardial surface and EGMs were calculated at each of these electrodes. The propagation of electrical wavefronts in the atrial myocardium during AF is very complex, so in order to correctly capture the wave propagation patterns, we have divided the EGMs into several short time intervals. Then a specific lattice for each of these periods was generated, and the cycles repeating in consecutive lattices tell us the location of the stable rotor. The respective atrial voltage map was used as a reference. By detecting a cycle between the same 3 nodes in 19 of the 58 networks, where 10 of these networks were in consecutive time intervals, a stable rotor was successfully located.