  Atrial fibrillation (AF) is a prevalent cardiac arrhythmia often treated
concomitantly with other cardiac interventions through the Cox Maze procedure.
This highly invasive intervention is still linked to a long-term recurrence
rate of approximately 35% in permanent AF patients. The aim of this study is to
preoperatively predict long-term AF recurrence post-surgery through the
analysis of atrial activity (AA) organization from non-invasive
electrocardiographic (ECG) recordings. A dataset comprising ECGs from 53
patients with permanent AF who had undergone Cox Maze concomitant surgery was
analyzed. The AA was extracted from the lead V1 of these recordings and then
characterized using novel predictors, such as the mean and standard deviation
of the relative wavelet energy (RWEm and RWEs) across different scales, and an
entropy-based metric that computes the stationary wavelet entropy variability
(SWEnV). The individual predictors exhibited limited predictive capabilities to
anticipate the outcome of the procedure, with the SWEnV yielding a
classification accuracy (Acc) of 68.07%. However, the assessment of the RWEs
for the seventh scale (RWEs7), which encompassed frequencies associated with
the AA, stood out as the most promising individual predictor, with sensitivity
(Se) and specificity (Sp) values of 80.83% and 67.09%, respectively, and an Acc
of almost 75%. Diverse multivariate decision tree-based models were constructed
for prediction, giving priority to simplicity in the interpretation of the
forecasting methodology. In fact, the combination of the SWEnV and RWEs7
consistently outperformed the individual predictors and excelled in predicting
post-surgery outcomes one year after the Cox Maze procedure, with Se, Sp, and
Acc values of approximately 80%, thus surpassing the results of previous
studies based on anatomical predictors associated with atrial function or
clinical data.
