Non-invasive prediction of catheter ablation outcome in persistent atrial fibrillation by exploiting the spatial diversity of surface ECG

Atrial fibrillation (AF) is the most common cardiac arrhythmia encountered in clinical practice. Radiofrequency catheter ablation (CA) is becoming one of the most widely employed therapies. Yet selection of patients who will benefit from this treatment remains a challenging task. Previous works have examined several electrocardiogram (ECG) parameters as potential predictors of CA success, such as fibrillatory wave (f-wave) amplitude. However, they require a manual computation and consider only a subset of electrodes, so inter-lead spatial variability of the 12-lead ECG is not fully exploited. The present study puts forward an automatic procedure for f-wave amplitude computation to non-invasively predict CA outcome. An extension of this quantitative measure to the whole set of leads is also proposed, based on Principal Component Analysis (PCA). We show that exploiting the spatial diversity present in the surface ECG not only improves the robustness to electrode selection but also increases the predictive power of the amplitude parameter.