Catheter Ablation Targeting Complex Fractionated Atrial Electrogram in Atrial Fibrillation

Surgery and Catheter Ablation for Atrial Fibrillation: History, Current Practice, and Future Directions

Atrial fibrillation (AF) is the most common of all cardiac arrhythmias, affecting roughly 1% of the general population in the Western world. The incidence of AF is predicted to double by 2050. Most patients with AF are treated with oral medications and only approximately 4% of AF patients are treated with interventional techniques, including catheter ablation and surgical ablation. The increasing prevalence and the morbidity/mortality associated with AF warrants a more aggressive approach to its treatment. It is the purpose of this invited editorial to describe the past, present, and anticipated future directions of the interventional therapy of AF, and to crystallize the problems that remain.

Spatiotemporal characteristics of atrial fibrillation electrograms: A novel marker for arrhythmia stability and termination

BACKGROUND

Sequentially mapped complex fractionated atrial electrograms (CFAE) and dominant frequency (DF) sites have been targeted during catheter ablation for atrial fibrillation (AF). However, these strategies have yielded variable success and have not been shown to correlate consistently with AF dynamics. Here, we evaluated whether the spatiotemporal stability of CFAE and DF may be a better marker of AF sustenance and termination.

METHODS

Eighteen sheep with 12 weeks of "one-kidney, one-clip" hypertension underwent open-chest studies. A total of 42 self-terminating (28-100 s) and 6 sustained (>15 min) AF episodes were mapped using a custom epicardial plaque and analyzed in 4-s epochs for CFAE, using the NavX CFE-m algorithm, and DF, using a Fast Fourier Transform. The spatiotemporal stability index (STSI) was calculated using the intraclass correlation coefficient of consecutive AF epochs.

RESULTS

A total of 67,733 AF epochs were analyzed. During AF initiation, mean CFE-m and the STSI of CFE-m/DF were similar between sustained and self-terminating episodes, although median DF was higher in sustained AF (p=0.001). During sustained AF, the STSI of CFE-m increased significantly (p=0.02), whereas mean CFE-m (p=0.5), median DF (p=0.07), and the STSI of DF remained unchanged (p=0.5). Prior to AF termination, the STSI of CFE-m was significantly lower (p<0.001), with a physiologically non-significant decrease in median DF (-0.3 Hz, p=0.006) and no significant changes in mean CFE-m (p=0.14) or the STSI of DF (p=0.06).

CONCLUSIONS

Spatiotemporal stabilization of CFAE favors AF sustenance and its destabilization heralds AF termination. The STSI of CFE-m is more representative of AF dynamics than are the STSI of DF, sequential mean CFE-m, or median DF.

Semi-supervised clustering of fractionated electrograms for electroanatomical atrial mapping

BACKGROUND

Electrogram-guided ablation procedures have been proposed as an alternative strategy consisting of either mapping and ablating focal sources or targeting complex fractionated electrograms in atrial fibrillation (AF). However, the incomplete understanding of the mechanism of AF makes difficult the decision of detecting the target sites. To date, feature extraction from electrograms is carried out mostly based on the time-domain morphology analysis and non-linear features. However, their combination has been reported to achieve better performance. Besides, most of the inferring approaches applied for identifying the levels of fractionation are supervised, which lack of an objective description of fractionation. This aspect complicates their application on EGM-guided ablation procedures.

METHODS

This work proposes a semi-supervised clustering method of four levels of fractionation. In particular, we make use of the spectral clustering that groups a set of widely used features extracted from atrial electrograms. We also introduce a new atrial-deflection-based feature to quantify the fractionated activity. Further, based on the sequential forward selection, we find the optimal subset that provides the highest performance in terms of the cluster validation. The method is tested on external validation of a labeled database. The generalization ability of the proposed training approach is tested to aid semi-supervised learning on unlabeled dataset associated with anatomical information recorded from three patients.

RESULTS

A joint set of four extracted features, based on two time-domain morphology analysis and two non-linear dynamics, are selected. To discriminate between four considered levels of fractionation, validation on a labeled database performs a suitable accuracy (77.6 %). Results show a congruence value of internal validation index among tested patients that is enough to reconstruct the patterns over the atria to located critical sites with the benefit of avoiding previous manual classification of AF types.

CONCLUSIONS

To the best knowledge of the authors, this is the first work reporting semi-supervised clustering for distinguishing patterns in fractionated electrograms. The proposed methodology provides high performance for the detection of unknown patterns associated with critical EGM morphologies. Particularly, obtained results of semi-supervised training show the advantage of demanding fewer labeled data and less training time without significantly compromising accuracy. This paper introduces a new method, providing an objective scheme that enables electro-physiologist to recognize the diverse EGM morphologies reliably.