Characterizing Surface Fibrillatory Waves through the Lagged Poincaré Plot for Preoperative Prediction of Ablation Success in Persistent Atrial Fibrillation

Atrial fibrillation (AF) management poses a significant challenge for healthcare systems due to the high midterm recurrence rate following catheter ablation (CA) for persistent AF.Analyzing the morphological variability of fibrillatory waves (f -waves) from the electrocardiogram (ECG) has provided insights about the atrial electrical activity organization, which is a crucial indicator for CA outcome prediction.This work introduces an innovative analysis of f -waves morphology evolution over time based on the lagged Poincar plots (PP) technique.Traditional ellipse-fitting quantifiers and centroid-derived PP features were evaluated in 94 f -waves excerpts (34 from patient that relapsed to AF 9 months after the intervention) reporting a higher predictive capability than common CA outcome predictors, such as the dominant frequency or the amplitude of the f -waves.Specifically, PP-based measures, such as the minor (SD1) and major (SD2) axes of the optimally fitted imaginary ellipse, their ratio (SD12) and the standard deviation of the distance between PP points and the distribution centroid (S d ) showed a predictive accuracy of 70-80%.The combination of PP features with time and frequency domain f -waves characteristics improved accuracy up to 85%.The lagged PP has proven to be a valuable tool in characterizing f -waves, paving the way for a more personalized approach in AF treatment.