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

Abstract

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.

Cardiac computational modelling

“…A modo de ejemplo, las oscilaciones de baja frecuencia de la repolarizacio ´n ventricular, propuestas recientemente para guiar el implante de un desfibrilador automa ´tico implantable profila ´ctico 37 , se han investigado mediante modelizacio ´n computacional y simulacio ´n para identificar sus bases celulares, establecer una relacio ´n con el riesgo de arritmia y relacionarlo con otros feno ´menos arritmoge ´nicos 38,39 . Otro reciente modelo electrofisiolo ´gico especı ´fico del paciente, denominado mapeo in silico de ritmo, predijo el origen en el tracto de salida derecho en las arritmias ventriculares idiopa ´ticas de 10/11 casos 40 . Tambie ´n se ha utilizado un modelo especı ´fico del paciente para investigar el mecanismo de microrreentrada que conduce a la generacio ´n de extrası ´stoles cerca de las a ´reas de infarto 41,42 .…”

Modelización computacional cardiaca

“…37,38 Another recent patientspecific electrophysiological model, so called in silico pace-mapping, predicted the right outflow tract origin in idiopathic ventricular arrhythmias in 10/11 cases. 39 A patient-specific model has also been used to investigate the mechanism of micro-reentry that leads to the generation of ectopic beats near infarct areas. 40,41 The simulation results explain how ectopic beats emerge from micro-reentries that are sustained by the heterogeneous structure of the infarct regions.…”

Cardiac Activation Maps Reconstruction: A Comparative Study Between Data-Driven and Physics-Based Methods

“…. ) Doste et al (2019). In Lozoya et al (2019), authors suggest an image-based machine learning approach to detect cardiac radio-frequency ablation targets.…”