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Predicting the Origin of Outflow Tract Ventricular Arrhythmias Using Machine Learning Techniques Trained With Patient-Specific Electrophysiological Simulations

Identifying the site of origin (SOO) of outflow tract ventricular arrhythmias (OTVAs) is key to plan radiofrequency ablation procedures. Currently, electrophysiologist try to extract that information pre-operatively from the ECG, and intraoperatively from electroanatomical maps. In this work, we study the prediction of the SOO by using machine learning approaches trained with patient-specific electrophysiological simulations of subjects that suffer OTVA. We built patient-specific models for 11 patients with OTVA, including an enhance description of the myofiber orientation in the outflow tracts, and simulated the sequence of activation, the BSPM and ECG. Following, we triggered the arrhythmia from twelve different SOO. Simulations were in agreement with real ECGs, hence we used the simulated ECGs to train our machine learning algorithms and classify the different SOO. According to our results, V3 lead provides useful information for SOO localization. Obtained classification rates show that simulated ECGs can help to determine right ventricle versus left ventricle outflow tract origin.