Non-invasive prediction of atrial fibrillation recurrence by recurrence quantification analysis on the fibrillation cycle length

The long-term success of atrial fibrillation (AF) ablation remains limited, primarily due to inter-patient variability in AF mechanisms. The ventricular residuals in ECG f-wave extraction, along with the low temporal resolution in Fourier spectral analysis, significantly impact dynamic structure analysis and may compromise the accuracy of AF recurrence prediction. To address these challenges, this work aims to improve the interpretation of recurring patterns in AF cycle length (AFCL) to aid in preoperative patient screening. The study utilized data from a dataset of 87 patients (77 with persistent AF and 10 with paroxysmal AF). The variability of AFCL was derived from the extracted f-waves of lead V1 in preprocedural 250-second recordings with EEMD-based cycle identification. Recurrence plot indices (RPIs) from recurrence quantification analysis were introduced to characterize the dynamic structure of AFCL variability. A support vector machine prediction model was subsequently applied in 10-fold cross-validation to incorporate multivariate RPIs with feature selection. RPIs showed significant differences between recurrence and non-recurrence patients. In ten-fold cross-validation, the sensitivity, specificity and accuracy of the prediction model were 75%, 100%, 90% for paroxysmal AF, and 66%, 75%, 71% for persistent AF. The recurrence prediction indicated significant differences in AF-free likelihood between patients predicted to recur and those predicted not, yielding p-values of 0.004 for paroxysmal AF and 0.001 for persistent AF. Non-invasive AFCL dynamics analysis showed effective prediction of long-term outcomes, suggesting their potential to aid in patient selection for optimal AF ablation benefits and reveal recurrence-related AF mechanisms. •Recurrence plot indices of cycle length variability correlate with ablation outcomes.•Dynamic analysis improved resolution to a single cycle, enhancing interpretability.•Prediction indices vary for paroxysmal vs. persistent AF, requiring separate models.