Predicting catheter ablation outcome in persistent atrial fibrillation using atrial dominant frequency and related spectral features.

Pre-procedural atrial fibrillation dominant frequency (AFDF) has been reported to play a role as a predictor of catheter ablation (CA) outcome for the treatment of persistent atrial fibrillation (AF). The present study analyzes some spectral features of the atrial signal aimed at evaluating the quality of surface AFDF estimation and discusses their predictive power. First, automated extraction of surface atrial activity (AA) on pre-procedural 12-lead ECG recordings is performed by means of an independent component analysis (ICA) method. AFDF is then estimated by means of short-time Fourier analysis of the extracted atrial sources and simultaneous endocardial electrograms (EGM) used as reference. On a database of 20 patients in persistent AF undergoing CA, AFDF does not appear to play a role as a predictor of CA outcome at follow-up, neither on ECG nor on EGM recordings. The quality of surface AFDF estimation is assessed by means of the correlation coefficient r between surface and EGM AFDF, as well as the spectral concentration (SC) of the estimated atrial signal. It is shown that the quality of surface AFDF estimation is significantly lower for non-terminating CA procedures, both in terms of r and SC. The latter, in particular, seems to play a significant role in distinguishing terminating from non-terminating CA procedures and therefore in the non-invasive prediction of CA outcome.