Idiopathic Ventricular Arrhythmia (IVA) presents challenges in treatment, often necessitating radiofrequency ablation when drug therapy proves ineffective. Precise localization of the effective ablation point remains crucial for successful intervention. Current methodologies rely heavily on electrograms (EGMs) acquired during the procedure, yet comprehensive studies on EGMs' dynamics near the ablation site are lacking. We address this gap by conducting a study to analyze EGMs' evolution proximal to the clinician-identified ablation point.
Our approach involves collecting a dataset of IVA patients undergoing ablation procedures, annotating the clinician-selected ablation points, and extracting EGM features. Leveraging CARTO3 technology by Biosense Webster, we capture EGMs alongside electrocardiograms (ECGs) for comprehensive analysis. Through meticulous signal processing, we synchronize ECG signals and identify key EGM landmarks, including the first peak of bipolar signals and positive/negative deflections in unipolar signals.
We introduce novel metrics such as precocity, measured as the temporal difference between the first bipolar peak and the ECG reference point. Additionally, we classify EGMs based on the presence or absence of positive deflections in unipolar signals. Longitudinal analysis reveals distinct patterns in EGM dynamics near the ablation point across multiple patients.
By correlating EGM characteristics with clinician-identified ablation points, our study aims to establish a robust set of rules for precise ablation point selection. This methodological refinement holds promise for streamlining radiofrequency ablation procedures, potentially reducing procedure times and recurrence rates. Ultimately, our work contributes to advancing treatment efficacy and patient outcomes in IVA management.