Novel Methods for Characterization of Paroxysmal Atrial Fibrillation in Human Left Atria

More effective methods for characterizing 3D electrical activity in human left atrium (LA) are needed to identify substrates/triggers and microreentrant circuit for paroxysmal atrial fibrillation (PAF). We describe a novel wavelet-based approach and wave-front centroid tracking that have been used to reconstruct regional activation frequency and electrical activation pathways from non-contact multi-electrode array.

Data from 13 patients acquired prior to ablation for PAF with a 64 electrode noncontact catheter positioned in the LA were analysed. Unipolar electrograms were reconstructed at 2048 locations across each LA endocardial surface. Weighted fine- and coarse-scale electrograms were constructed by wavelet decomposition and combined with peak detection to identify atrial fibrillation (AF) activation frequency and fractionated activity at each site. LA regions with upper quartile AF frequencies were identified for each patient. On the other hand, a wave-front centroid tracking approach was introduced for this first time to detect macro-reentrant circuit during PAF.

The results employing wavelet-based analysis on atrial unipolar electrograms are validated by the signals recorded simultaneously via the contacted ablation catheter and visually tracking the 3D spread of activation through the interest region. Multiple connected regions of high frequency electrical activity were seen; most often in left superior pulmonary vein (10/12), septum (9/12) and atrial roof (9/12), as well as the ridge (8/12). The wave-front centroid tracking approach detects a major macro circuit involving LPVs, PLA, atrial floor, MV, septum, atrial roof and ridge. The regions with high frequency by wave-front tracking are consistent with the results using wavelet approach and our clinical observations.

The wavelet-based technique and wave-front centroid tracking approach provide a robust means of extracting spatio-temporal characteristics of PAF. The approach could facilitate accurate identification of pro-arrhythmic substrate and triggers, and therefore, to improve success rate of catheter ablation for AF.