Automatic quality electrogram assessment improves phase-based reentrant activity identification in atrial fibrillation.

Identification of reentrant activity driving atrial fibrillation (AF) is increasingly important to ablative therapies. The goal of this work is to study how the automatically-classified quality of the electrograms (EGMs) affects reentrant AF driver localization. EGMs from 259 AF episodes obtained from 29 AF patients were recorded using 64-poles basket catheters and were manually classified according to their quality. An algorithm capable of identifying signal quality was developed using time and spectral domain parameters. Electrical reentries were identified in 3D phase maps using phase transform and were compared with those obtained with a 2D activation-based method. Effect of EGM quality was studied by discarding 3D phase reentries detected in regions with low-quality EGMs. Removal of reentries identified by 3D phase analysis in regions with low-quality EGMs improved its performance, increasing the area under the ROC curve (AUC) from 0.69 to 0.80. The EGMs quality classification algorithm showed an accurate performance for EGM classification (AUC 0.94) and reentry detection (AUC 0.80). Automatic classification of EGM quality based on time and spectral signal parameters is feasible and accurate, avoiding the manual labelling. Discard of reentries identified in regions with automatically-detected poor-quality EGMs improved the specificity of the 3D phase-based method for AF driver identification.

Noninvasive Assessment of Complexity of Atrial Fibrillation: Correlation With Contact Mapping and Impact of Ablation.

BACKGROUND: It is difficult to noninvasively phenotype atrial fibrillation (AF) in a way that reflects clinical end points such as response to therapy. We set out to map electrical patterns of disorganization and regions of reentrant activity in AF from the body surface using electrocardiographic imaging, calibrated to panoramic intracardiac recordings and referenced to AF termination by ablation. METHODS: Bi-atrial intracardiac electrograms of 47 patients with AF at ablation (30 persistent, 29 male, 63±9 years) were recorded with 64-pole basket catheters and simultaneous 57-lead body surface ECGs. Atrial epicardial electrical activity was reconstructed and organized sites were invasively and noninvasively tracked in 3-dimension using phase singularity. In a subset of 17 patients, sites of AF organization were targeted for ablation. RESULTS: Body surface mapping showed greater AF organization near intracardially detected drivers than elsewhere, both in phase singularity density (2.3±2.1 versus 1.9±1.6; P=0.02) and number of drivers (3.2±2.3 versus 2.7±1.7; P=0.02). Complexity, defined as the number of stable AF reentrant sites, was concordant between noninvasive and invasive methods (r2=0.5; CC=0.71). In the subset receiving targeted ablation, AF complexity showed lower values in those in whom AF terminated than those in whom AF did not terminate (P<0.01). CONCLUSIONS: AF complexity tracked noninvasively correlates well with organized and disorganized regions detected by panoramic intracardiac mapping and correlates with the acute outcome by ablation. This approach may assist in bedside monitoring of therapy or in improving the efficacy of ongoing ablation procedures.

Noninvasive assessment of the complexity and stationarity of the atrial wavefront patterns during atrial fibrillation.

A novel automated approach to quantitatively evaluate the degree of spatio-temporal organization in the atrial activity (AA) during atrial fibrillation (AF) from surface recordings, obtained from body surface potential maps (BSPM), is presented. AA organization is assessed by measuring the reflection of the spatial complexity and temporal stationarity of the wavefront patterns propagating inside the atria on the surface ECG, by means of principal component analysis (PCA). Complexity and stationarity are quantified through novel parameters describing the structure of the mixing matrices derived by the PCA of the different AA segments across the BSPM recording. A significant inverse correlation between complexity and stationarity is highlighted by this analysis. The discriminatory power of the parameters in identifying different groups in the set of patients under study is also analyzed. The obtained results present analogies with earlier invasive studies in terms of number of significant components necessary to describe 95% of the variance in the AA (four for more organized AF, and eight for more disorganized AF). These findings suggest that automated analysis of AF organization exploiting spatial diversity in surface recordings is indeed possible, potentially leading to an improvement in clinical decision making and AF treatment.