Nonlinear Analysis of Fibrillatory Electrogram Similarity to Optimize the Detection of Complex Fractionated Electrograms During Persistent Atrial Fibrillation

Abstract

Currently, the identification of complex fractionated atrial electrograms (CFEs) in the substrate modification is mostly based on cycle length-derived algorithms. The characteristics of the fibrillation electrogram morphology and their consistency over time are not clear. The aim of this study was to optimize the detection algorithm of crucial CFEs by using nonlinear measure electrogram similarity. One hundred persistent atrial fibrillation patients that underwent catheter ablation were included. In patients who required CFE ablation (79%), the time-domain fibrillation signals (6 seconds) were acquired for a linear analysis (mean fractionation interval and dominant frequency [DF]) and nonlinear-based waveform similarity analysis of the local electrograms, termed the similarity index (SI). Continuous CFEs were targeted with an endpoint of termination. Predictors of the various signal characteristics on the termination and clinical outcome were investigated. Procedural termination was observed in 39% and long-term sinus rhythm maintenance in 67% of the patients. The targeted CFEs didn't differ based on the linear analysis modalities between the patients who responded and did not respond to CFE ablation. In contrast, the average SI of the targeted CFEs was higher in termination patients, and they had a better outcome. Multivariate regression analysis showed that a higher SI independently predicted sites of termination (≥ 0.57; OR = 4.9; 95% CI = 1.33-18.0; P = 0.017). In persistent AF patients, a cycle length-based linear analysis could not differentiate culprit CFEs from bystanders. This study suggested that sites with a high level of fibrillation electrogram similarity at the CFE sites were important for AF maintenance.