Catheter ablation outcome prediction in persistent atrial fibrillation based on spatio-temporal complexity measures of the surface ECG

Radiofrequency catheter ablation (CA) is increasingly employed to treat atrial fibrillation (AF), yet selection of patients who would actually benefit from this therapy still remains an open issue. The present work introduces some non-invasive quantitative parameters to discriminate between successful and failing CA procedures by exploiting the spatial diversity of the 12-lead surface ECG. They are based on the normalized mean square error (NMSE) between consecutive atrial activity ECG signal segments and their rank-n approximations determined by principal component analysis. As opposed to the single-lead approach of previous works, we consider NMSE values computed on more than one lead. One such multilead-based parameter is able to distinguish between successful and failing ablations before performing the procedure with AUC=0.86, p value=7·10^-5. This study demonstrates that the proposed multilead parameters can effectively predict CA outcome and potentially contribute to more accurate patient selection strategies for this AF therapy.