Nonnegative Matrix Factorization for Noninvasive Prediction of Catheter Ablation Outcome in Persistent Atrial Fibrillation

Despite the increasing popularity of catheter ablation (CA) for treat- ing atrial fibrillation (AF), the identification of patients who would actually benefit from the therapy remains a challenging open issue. This study aims at noninvasively predicting CA outcome by quanti- fying the spatio-temporal variability of the atrial activity signal mea- sured on the standard 12-lead electrocardiogram. The normalized mean square error (NMSE) between consecutive atrial segments and their principal component approximations is computed for each lead, as a recent noninvasive index of AF organization. In the present work, the multilead NMSE array is decomposed by means of a non- negative matrix factorization (NNMF) with two different initializa- tions. The reconstruction error between the original NMSE matrix and its low-rank NNMF approximation is taken as a classification feature. A dataset of persistent AF patients undergoing CA reveals that the proposed feature is able to predict the therapy's outcome with a notably higher level of statistical significance than recent single-lead indices.