Abstract : Radiofrequency catheter ablation (CA) is increasingly employed to treat persistent atrial fibrillation (AF), yet selection of patients who would positively respond to this therapy is currently a critical problem. Several parameters of the surface 12-lead electrocardiogram (ECG) have been analyzed in previous works to predict AF termination by CA. Nevertheless, they are affected by some limitations, such as manual computation and the examination of a single ECG lead while neglecting contributions from other electrodes. AF spatio-temporal organization has been described on surface ECG by means of the normalized mean square error (NMSE) between consecutive atrial activity (AA) signal segments and their reduced-rank approximations based on principal component analysis (PCA). However, these features do not show to be correlated with CA outcome. In this study, such descriptors are adequately adapted and applied to CA outcome prediction. An NMSE index is put forward, computed over the set of eight linearly independent ECG leads after AA signal rank-1 approximations determined by weighted principal component analysis (WPCA). The final predictor is able to discriminate between successful (70.76 ± 17.74) and failing CA procedures (37.54 ± 20.01) before performing the ablation (p-value = 0.0013, AUC = 0.91). The proposed WPCA-based technique emphasizes the most descriptive components of AF electrophysiology by selectively enhancing contributions coming from the most representative ECG leads. Our investigation confirms that ECG spatial diversity exploitation in this WPCA-based framework not only endows the NMSE index with clinical value in the context of CA outcome prediction, but it also improves classification accuracy and increases robustness to ECG lead selection.