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

Abstract

To differentiate electrograms representing sites of active atrial fibrillation (AF) drivers from passive ones. 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). 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). 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). Active CFAEs critical to AF maintenance exhibit higher Egm-C recurrence and can be differentiated from passive bystander CFAE sites using RQA.