A Machine Learning Approach to Automated Localization of Targets for Ventricular Tachycardia Ablation Using Sinus Rhythm Signal Features

Catheter ablation has the potential to become an effective treatment for ventricular tachycardia (VT), but the current identification of ablation sites relies on the operator's judgement and experience.This study proposes a novel machine learning approach to identify ablation targets based on signal features derived from intracardiac electrograms recorded in sinus rhythm.56 substrate maps were collected during pacing and sinus rhythm using a multipolar catheter (Advisor HD grid, Ensite Precision) in 13 pigs with chronic myocardial infarction (n=31,515 mapping points).36 VTs were induced and critical components of the VT circuit including early-, mid-and late-diastolic signals, were localized.Cardiac sites within 6 mm from these critical VT sites were considered as potential ablation targets (7.3% of all cardiac sites).46 features representing signal morphology, function, spatial and spectral properties were extracted from each bipolar and unipolar signal recorded during pacing or sinus rhythm.A random forest algorithm was trained on 80% of the data to identify the 20 most important features and 10 times 10-fold cross-validation was used to identify the best model.Validation on the remaining 20% of data showed an area under the ROC curve of 77.8%, and both 70% of sensitivity and specificity, for the best model.This study demonstrates for the first time that machine learning may support clinicians in the localization of targets for VT ablation.