Correlation-based Estimation of Activation Frequency in Intracardiac and ECG Signals During Atrial Fibrillation

Atrial Fibrillation (AF) is the most prevalent cardiac arrhythmia, affecting 2% of the population and having a significant impact on health and the economy.Despite advances in therapies, optimal treatment remains elusive due to variable success rates and subjective decision-making in ablation protocols.This study aimed to address these issues by non-invasively determining the modal Atrial Cycle Length (ACL) values in both atria of AF patients to improve ablation therapy.Body Surface Potential Mapping (BSPM) and Intracardiac Electrograms (EGM) were recorded simultaneously in 22 patients, and different correlation-based methods were used to identify ACL.The results of the analysis indicated that correlation with five templates, achieved a relative error of 15% compared to the manual annotation.Furthermore, the custom metrics demonstrated a 1.2 ms difference and 97% agreement between the intra-atrial and surface recordings.Additionally, machine learning models predicted modal intra-ACL from surface data with a relative error of 7%.Clinically, estimating intra-ACL from surface measurements will enable rapid AF characterization to personalise therapy.