A Multi-Variate Predictability Framework to Assess Invasive Cardiac Activity and Interactions during Atrial Fibrillation

This study introduces a predictability framework based on the concept of Granger causality (GC), in order to ana-lyze the activity and interactions between different intracardiac sites during atrial fibrillation (AF). GC-based interactions were studied using a three-electrode analysis scheme with multi-variate auto-regressive models of the involved pre-processed intracardiac signals. The method was evaluated in different scenarios covering simulations of complex atrial activity as well as endocardial sig-nals acquired from patients. Results illustrate the ability of the method to determine atrial rhythm complexity and to track and map propagation during AF. The proposed framework provides information on the underlying activation and regularity, does not require activation detection or post-processing algorithms and is applicable for the analysis of any multi-electrode catheter.