Artificial intelligence analysis of ECG signals to predict arrhythmia recurrence after cryoballoon ablation of atrial fibrillation.

Cryoballoon-based pulmonary vein isolation (CB) is an effective option for rhythm control in atrial fibrillation (AF). There have been multiple attempts to predict arrhythmia recurrence, but with moderate success.To use artificial intelligence (AI) deep electrocardiogram (ECG) analysis to predict arrythmia recurrence after CB.In a single-center study of 250 consecutive pts (58.2±12.6 years, 30\\% female) treated with CB for AF (05.2017–04.2019), 60\\% had paroxysmal AF (PAF), 66.5\\% had hypertension, and 27.2\\% had a redo CB. Analyses included left atrial volume (LA vol: indexed for BSA and assed by angio-MSCT in 76\\% and the rest with echo), left ventricle ejection fraction (LVEF) and hypertrophy (LVH: septal/posterior wall thickness ≥11mm in ♂ and ≥10mm in ♀) along with 30s arrhythmia recurrence at 2-yr follow-up. Baseline 500Hz raw 12-lead digital ECG signals were analyzed by means of convolutional neural network (CNN) architecture that was taught to process the 12-channel ECG signal (XML). The transfer learning method of CNN parameters learned on a very large number of coded consecutive training ECG samples (n=1,000) was adopted to analyze current sample of ECG signals.Arrhythmia recurrence at 2-yrs was 46.0\\% (n=115). There was gradual increase in predictive performance being the lowest if only baseline clinical data were analyzed and the highest combing all baseline clinical data plus anatomical and functional parameters (Table 1: #1–#3). AI baseline ECG analysis alone offered predictive performance similar to that made upon analysis of all baseline clinical data or joint analysis of LA vol, LVH, and LVEF (Table 1: #4). AI results added substantially to predictive performance of models using baseline clinical data alone (#1 vs #5, p\\<0.001) and joint analysis of baseline clinical plus LA vol, LVH and LVEF (#3 vs #6, p=0.0130) (Fig. 1).Prediction of recurrence after CB using raw ECG data with deep AI analysis is feasible. Joint analysis of results of AI of baseline ECG and basic clinical data offers predictive performance similar to that made upon analysis of clinical data including advanced information on LA volume and LVH and function. AI analysis of baseline ECG adds significantly to models aimed at recurrent AF prediction.Type of funding sources: Public hospital(s). Main funding source(s): National Institute of Cardiology, Warsaw, PolandTable 1Figure 1