Analysis of the Endocavitary Electrical Signal and Surface Electrocardiogram to Determine Relevant Features for an Accurate Prediction of Ablation Outcome for Patients in Persistent Atrial Fibrillation

Background :

Despite recent progress in the management of patients with Atrial Fibrillation (AF), AF remains one of the main risk factor of stroke, heart failure and cardiovascular mortality in the world. AF is the most common cardiac arrhythmia, and its prevalence is expected to grow in the coming years.

Catheter ablation (CA) of AF can be successful in restoring and maintaining Sinus rhythm (SR) in patients with paroxysmal, and persistent AF. Haissaguerre et al. have shown that AF ablation of the heart by isolating the Pulmonary Vein is usually sufficient. However, when ablating persistent AF patients ablating pathological regions in the atria is also required. CA is still a challenging procedure, and reported success rate for persistent AF patients remains relatively low (around 60%). This procedure is associated with risks inherent to any invasive cardiac procedures.

A better identification of good responders would reduce the risks associated with ablation, especially for patients with low odds of favorable outcome, while also increasing the success rate of the procedure. It has been shown in the literature the long-term CA outcome can be predicted non-invasively by atrial fibrillatory wave (f-wave) amplitudes.

Hypothesis :

We assume that a morphological analysis of the f-waves on standard twelve ECG will allow for a better CA outcome prediction.

Objectives :

The aim of this study is to automatically analyse the morphology of the ECG signal before a CA procedure, extract relevant features for an accurate prediction of long-term CA outcome for patients in persistent AF.