Purpose Recent investigations failed to reproduce the positive rotor-guided ablation outcomes shown by initial studies for treating persistent atrial fibrillation (persAF). Phase singularity (PS) is an important feature for AF driver detection, but algorithms for automated PS identification differ. We aim to investigate the performance of four different techniques for automated PS detection. Methods 2048-channel virtual electrogram (VEGM) and electrocardiogram signals were collected for 30 s from 10 patients undergoing persAF ablation. QRST-subtraction was performed and VEGMs were processed using sinusoidal wavelet reconstruction. The phase was obtained using Hilbert transform. PSs were detected using four algorithms: (1) 2D image processing based and neighbor-indexing algorithm; (2) 3D neighbor-indexing algorithm; (3) 2D kernel convolutional algorithm estimating topological charge; (4) topological charge estimation on 3D mesh. PS annotations were compared using the structural similarity index (SSIM) and Pearson’s correlation coefficient (CORR). Optimized parameters to improve detection accuracy were found for all four algorithms using F β score and 10-fold cross-validation compared with manual annotation. Local clustering with density-based spatial clustering of applications with noise (DBSCAN) was proposed to improve algorithms 3 and 4. Results The PS density maps created by each algorithm with default parameters were poorly correlated. Phase gradient threshold and search radius (or kernels) were shown to affect PS detections. The processing times for the algorithms were significantly different ( p < 0.0001). The F β scores for algorithms 1, 2, 3, 3 + DBSCAN, 4 and 4 + DBSCAN were 0.547, 0.645, 0.742, 0.828, 0.656, and 0.831. Algorithm 4 + DBSCAN achieved the best classification performance with acceptable processing time (2.0 ± 0.3 s). Conclusion AF driver identification is dependent on the PS detection algorithms and their parameters, which could explain some of the inconsistencies in rotor-guided ablation outcomes in different studies. For 3D triangulated meshes, algorithm 4 + DBSCAN with optimal parameters was the best solution for real-time, automated PS detection due to accuracy and speed. Similarly, algorithm 3 + DBSCAN with optimal parameters is preferred for uniform 2D meshes. Such algorithms – and parameters – should be preferred in future clinical studies for identifying AF drivers and minimizing methodological heterogeneities. This would facilitate comparisons in rotor-guided ablation outcomes in future works.

Background Atrial fibrillation (AF) driver mechanisms are obscured to clinical multielectrode mapping approaches that provide partial, surface‐only visualization of unstable 3‐dimensional atrial conduction. We hypothesized that transient modulation of refractoriness by pharmacologic challenge during multielectrode mapping improves visualization of hidden paths of reentrant AF drivers for targeted ablation. Methods and Results Pharmacologic challenge with adenosine was tested in ex vivo human hearts with a history of AF and cardiac diseases by multielectrode and high‐resolution subsurface near‐infrared optical mapping, integrated with 3‐dimensional structural imaging and heart‐specific computational simulations. Adenosine challenge was also studied on acutely terminated AF drivers in 10 patients with persistent AF. Ex vivo, adenosine stabilized reentrant driver paths within arrhythmogenic fibrotic hubs and improved visualization of reentrant paths, previously seen as focal or unstable breakthrough activation pattern, for targeted AF ablation. Computational simulations suggested that shortening of atrial refractoriness by adenosine may (1) improve driver stability by annihilating spatially unstable functional blocks and tightening reentrant circuits around fibrotic substrates, thus unmasking the common reentrant path; and (2) destabilize already stable reentrant drivers along fibrotic substrates by accelerating competing fibrillatory wavelets or secondary drivers. In patients with persistent AF, adenosine challenge unmasked hidden common reentry paths (9/15 AF drivers, 41±26% to 68±25% visualization), but worsened visualization of previously visible reentry paths (6/15, 74±14% to 34±12%). AF driver ablation led to acute termination of AF. Conclusions Our ex vivo to in vivo human translational study suggests that transiently altering atrial refractoriness can stabilize reentrant paths and unmask arrhythmogenic hubs to guide targeted AF driver ablation treatment.

Non-invasive analysis of atrial fibrillation (AF) using body surface mapping (BSM) has gained significant interest, with attempts at interpreting atrial spectro-temporal parameters from body surface signals. As these body surface signals could be affected by properties of the torso volume conductor, this interpretation is not always straightforward. This paper highlights the volume conductor effects and influences of the algorithm parameters for identifying the dominant frequency (DF) from cardiac signals collected simultaneously on the torso and atrial surface. Bi-atrial virtual electrograms (VEGMs) and BSMs were recorded simultaneously for 5 min from 10 patients undergoing ablation for persistent AF. Frequency analysis was performed on 4 s segments. DF was defined as the frequency with highest power between 4 and 10 Hz with and without applying organization index (OI) thresholds. The volume conductor effect was assessed by analyzing the highest DF (HDF) difference of each VEGM HDF against its BSM counterpart. Significant differences in HDF values between intra-cardiac and torso signals could be observed, independent of OI threshold. This difference increases with increasing endocardial HDF (BSM-VEGM median difference from −0.13 Hz for VEGM HDF at 6.25 ± 0.25 Hz to −4.24 Hz at 9.75 ± 0.25 Hz), thereby confirming the theory of the volume conductor effect in real-life situations. Applying an OI threshold strongly affected the BSM HDF area size and location and atrial HDF area location. These results suggest that volume conductor and measurement algorithm effects must be considered for appropriate clinical interpretation.

Purpose: Complex fractionated atrial electrograms (CFAE)-guided ablation after pulmonary vein isolation (PVI) has been used for persistent atrial fibrillation (persAF) therapy. This strategy has shown suboptimal outcomes due to, among other factors, undetected changes in the atrial tissue following PVI. In the present work, we investigate CFAE distribution before and after PVI in patients with persAF using a multivariate statistical model.Methods: 207 pairs of atrial electrograms (AEGs) were collected before and after PVI respectively, from corresponding LA regions in 18 persAF patients. Twelve attributes were measured from the AEGs, before and after PVI. Statistical models based on multivariate analysis of variance (MANOVA) and linear discriminant analysis (LDA) have been used to characterize the atrial regions and AEGs.Results: PVI significantly reduced CFAEs in the LA (70 vs. 40%; P < 0.0001). Four types of LA regions were identified, based on the AEGs characteristics: (i) fractionated before PVI that remained fractionated after PVI (31% of the collected points); (ii) fractionated that converted to normal (39%); (iii) normal prior to PVI that became fractionated (9%) and; (iv) normal that remained normal (21%). Individually, the attributes failed to distinguish these LA regions, but multivariate statistical models were effective in their discrimination (P < 0.0001).Conclusion: Our results have unveiled that there are LA regions resistant to PVI, while others are affected by it. Although, traditional methods were unable to identify these different regions, the proposed multivariate statistical model discriminated LA regions resistant to PVI from those affected by it without prior ablation information.

The unstable temporal behavior of atrial electrical activity during persistent atrial fibrillation (persAF) might influence ablation target identification, which could explain the conflicting persAF ablation outcomes in previous studies. We sought to investigate the temporal behavior and consistency of atrial electrogram (AEG) fractionation using different segment lengths. Seven hundred ninety-seven bipolar AEGs were collected with three segment lengths (2.5, 5,and 8 s) from 18 patients undergoing persAF ablation. The AEGs with 8-s duration were divided into three 2.5-s consecutive segments. AEG fractionation classification was applied off-line to all cases following the CARTO criteria; 43% of the AEGs remained fractionated for the three consecutive AEG segments, while nearly 30% were temporally unstable. AEG classification within the consecutive segments had moderate correlation (segment 1 vs 2: Spearman’s correlation ρ = 0.74, kappa score κ = 0.62; segment 1 vs 3: ρ = 0.726, κ = 0.62; segment 2 vs 3: ρ = 0.75, κ = 0.68). AEG classifications were more similar between AEGs with 5 and 8 s (ρ = 0.96, κ = 0.87) than 2.5 versus 5 s (ρ = 0.93, κ = 0.84) and 2.5 versus 8 s (ρ = 0.90, κ = 0.78). Our results show that the CARTO criteria should be revisited and consider recording duration longer than 2.5 s for consistent ablation target identification in persAF.Electronic supplementary materialThe online version of this article (doi:10.1007/s11517-017-1667-1) contains supplementary material, which is available to authorized users.