A Model Population-Based Approach to Enhance the Detection of Premature Ventricular Contraction of ECGI

Premature ventricular contractions (PVCs) represent a prevalent cardiac anomaly affecting both individuals with and without structural heart disease, with outflow tractoriginated PVCs constituting the majority of idiopathic ventricular arrhythmias.Despite the commonality and potential severity of PVCs, including their role in ventricular fibrillation, accurate diagnosis, and localization remain challenging due to the limitations of traditional 12-lead ECG and the use of rule-based decision algorithms.Electrocardiographic imaging (ECGI) has been introduced as an innovative method to assist in PVC localization, albeit with limitations in pinpointing exact origins.In this study, we explore the power of computer models to create a population database of PVC to refine treatment approaches through an enhanced understanding of electrical propagation dynamics.Through a database of simulated PVCs, we developed a PVC estimation algorithm capable of accurately estimating PVC origins.Our approach offers a promising avenue for personalized and more effective treatment strategies in managing PVC-induced arrhythmias.