  Introduction: 12-lead electrocardiogram (ECG) is recorded during atrial
fibrillation (AF) catheter ablation procedure (CAP). It is not easy to
determine if CAP was successful without a long follow-up assessing for AF
recurrence (AFR). Therefore, an AFR risk prediction algorithm could enable a
better management of CAP patients. In this research, we extracted features from
12-lead ECG recorded before and after CAP and train an AFR risk prediction
machine learning model. Methods: Pre- and post-CAP segments were extracted from
112 patients. The analysis included a signal quality criterion, heart rate
variability and morphological biomarkers engineered from the 12-lead ECG (804
features overall). 43 out of the 112 patients (n) had AFR clinical endpoint
available. These were utilized to assess the feasibility of AFR risk
prediction, using either pre or post CAP features. A random forest classifier
was trained within a nested cross validation framework. Results: 36 features
were found statistically significant for distinguishing between the pre and
post surgery states (n=112). For the classification, an area under the receiver
operating characteristic (AUROC) curve was reported with AUROC_pre=0.64 and
AUROC_post=0.74 (n=43). Discussion and conclusions: This preliminary analysis
showed the feasibility of AFR risk prediction. Such a model could be used to
improve CAP management.
