Key result
Machine learning algorithms trained on patient-specific electrophysiological simulations achieved up to 89% accuracy in classifying the site of origin of outflow tract ventricular arrhythmias.
Why the study?
Identifying the site of origin of outflow tract ventricular arrhythmias is key to planning radiofrequency ablation procedures.
Can machine learning algorithms trained on patient-specific electrophysiological simulations accurately predict the site of origin of outflow tract ventricular arrhythmias?
Can machine learning algorithms trained on patient-specific electrophysiological simulations accurately predict the site of origin of outflow tract ventricular arrhythmias?
Machine learning models trained on patient-specific electrophysiological simulations can accurately predict the site of origin of outflow tract ventricular arrhythmias using specific precordial ECG leads.
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 patientspecific 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.
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Doste et al. (2019) studied Outflow tract ventricular arrhythmias (n=11). Machine learning algorithms trained with patient-specific electrophysiological simulations was evaluated on Accuracy in classifying RVOT vs LVOT origin using k-NN classifier with V2-V5 leads. Machine learning algorithms trained on patient-specific electrophysiological simulations achieved up to 89% accuracy in classifying the site of origin of outflow tract ventricular arrhythmias.
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