Atrial Fibrillation Recurrence Risk Prediction From 12-Lead ECG Recorded Pre- and Post-Ablation Procedure

Abstract

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 re-currence (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 pre-diction machine learning model. Methods: Pre- and post-CAP segments were extracted from 112 patients. The anal-ysis included a signal quality criterion, heart rate variabil-ity 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 clas-sifier was trained within a nested cross validation frame-work. Results: 36 features were found statistically signif-icant for distinguishing between the pre and post surgery states (n=112). For the classification, an area under the receiver operating characteristic (AUROC) curve was re-ported with AU ROCpre= 0.64 and AU ROCpost= 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.