Nonlinear interdependence of electrograms as a tool to characterize propagation patterns in atrial fibrillation

Abstract

Ablation of stable sources is a promising treatment to reverse the atrial fibrillation (AF) although its underlying mechanisms are still debated. Many attempts have been made to detect stable spiral waves as a target of ablation through univariate processing of intracardiac electrogram signals (EGM). Few researchers have addressed the bivariate assessment in AF, and the existing studies are mainly based on the phase-locking value (PLV). The present work introduces a scheme to assess the AF conduction patterns through the nonlinear interdependence index. Two and three-dimensional computational simulations of cardiac conduction are used to assess the proposed method in characterizing the dynamics of fibrillatory mechanisms such as rotors. In addition, a study with real signals acquired from an AF patient is shown as an example of a possible application in humans. In the simulated episodes, the nonlinear interdependence index characterizes the core of the stable rotors as a region having low interdependence values, surrounded by a high synchronization region. In episodes where multiple reentries coexist, the nonlinear interdependence maps highlight regions harboring the core of stable and transient rotors. Additionally, we found a positive correlation between PLV and nonlinear interdependence index in both real and virtual EGM. The proposed method based on nonlinear synchronization analysis renders relevant information about the fibrillatory dynamics and it is 7.8 times faster than PLV. Hence, the nonlinear interdependence index represents a potential alternative to phase synchronization methods for characterizing AF dynamics.