Recurrence quantification analysis of complex‐fractionated electrograms differentiates active and passive sites during atrial fibrillation

Abstract

Objectives To differentiate electrograms representing sites of active atrial fibrillation (AF) drivers from passive ones. Background Ablation of complex‐fractionated atrial electrograms (CFAEs) is controversial due to difficulty in distinguishing CFAEs representing sites of active AF drivers from passive mechanisms. We hypothesized that active CFAE sites exhibit repetitive wavefront directionality, thereby inscribing an electrogram conformation (Egm‐C) that is more recurrent compared with passive CFAE sites; and that can be differentiated from passive CFAEs using nonlinear recurrence quantification analysis (RQA). Methods We developed multiple computer models of active CFAE mechanisms (ie, rotors) and passive CFAE mechanisms (ie, wavebreak, slow conduction, and double potentials). CFAE signals were converted into discrete time‐series representing Egm‐C. The RQA algorithm was used to compare signals derived from active CFAE sites to those from passive CFAEs sites. The RQA algorithm was then applied to human CFAE signals collected during AF ablation (n = 17 patients). Results RQA was performed in silico on simulated bipolar CFAEs within active (n = 45) and passive (n = 60) areas. Recurrence of Egm‐C was significantly higher in active compared with passive CFAE sites (31.8% ± 19.6% vs 0.3% ± 0.5%, respectively, P < .0001) despite no difference in mean cycle length (CL). Similarly, for human AF (n = 39 signals), Egm‐C recurrence was higher in active vs passive CFAE areas despite similar CLs (%recurrence 13.6% ± 15.5% vs 0.1% ± 0.3%, P < .002; mean CL 102.5 ± 14.3 vs 106.6 ± 14.4, P = NS). Conclusion Active CFAEs critical to AF maintenance exhibit higher Egm‐C recurrence and can be differentiated from passive bystander CFAE sites using RQA.

Pitfalls in the definition of complex fractionated atrial electrograms for atrial fibrillation studies

“…We read with great interest the study by Baher et al 1 Their work helps to consolidate the use of recurrence quantification analysis (RQA) in the investigation of cardiac dynamics during atrial fibrillation (AF). It also motivates further discussion on creative methods and biomarkers for studying such complex cardiac disorder.…”

Preventing recurrence through analysing recurrence

“…The study by Baher et al in this issue of Journal of Cardiovascular Electrophysiology , aims to automatically distinguish CFAEs originating from active versus passive tissue sites by means of directly quantifying how recurrent a particular electrogram confirmation is. They do so through the use of recurrence quantification analysis (RQA), a standard signal processing technique used to evaluate recurring patterns within complex signals.…”

“…There are multiple definitions of CFAE in the literature and different electroanatomic mapping systems implement different algorithms for calculating the degree of electrogram fractionation, which is likely a confounding factor for the lack of consistency between findings . Baher et al consider simulated electrograms with mean cycle length (CL) <100 ms as CFAE, while clinical CFAE were those with mean CL <120 ms, where mean CL was calculated using the EnSite system subject to a refractory period of 40 ms and maximum electrogram width of 15 ms. The definition of active and passive mechanisms is also paramount to interpreting the study findings.…”

“…Baher et al should be congratulated on the careful combination of detailed in silico testing of their algorithm, along with later direct clinical testing and validation in the same study. Their findings highlight the importance of increased mechanistic understanding of electrogram morphology for improving ablation therapy, and the potential of applying such automated signal processing algorithms in the clinical environment.…”

“…There are multiple definitions of CFAE in the literature and different electroanatomic mapping systems implement different algorithms for calculating the degree of electrogram fractionation, which is likely a confounding factor for the lack of consistency between findings. 11 Baher et al 10…”