Key result
The gain in eigenvalue dominance produced by ensemble alignment (∆RA) accurately detected atrial fibrosis in a simulated model, achieving an area under the curve of 0.98 and an accuracy of 1.
Why the study?
Using low bipolar electrogram peak-to-peak voltage to identify scar tissue for AF substrate ablation ignores spatiotemporal information and depends on catheter orientation.
Effect estimate: AUC 0.98
p-value: p=<0.05
Eigenvalue dominance ratio analysis of aligned unipolar electrograms accurately identifies atrial fibrosis in simulated tissue, offering a potential improvement over standard bipolar voltage mapping.
No change to clinical mapping practice; leaves open validation of eigenvalue analysis for atrial fibrosis in humans.
Atrial fibrosis plays an important role in the pathogenesis of atrial fibrillation (AF). Low bipolar electrograms (b-EGMs) peak-to-peak voltage areas indicate scar tissue and are considered targets for AF substrate ablation. However, this approach ignores the spatiotemporal information embedded in the signal and the dependence of b-EGMs on catheter orientation. This work proposes an approach to detect fibrosis based on the eigenvalue dominance ratio (EIGDR) in an ensemble (clique) of unipolar electrograms (u-EGMs). A 2-D tissue with a central circular patch of fibrosis has been simulated using the Courtemanche cellular model. Maps of EIGDR have been computed using two sizes of electrode cliques, from the original u-EGMs within the ensemble or after a time alignment of these signals.
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Riccio et al. (2020) studied Atrial fibrosis. Eigenvalue dominance ratio (EIGDR) analysis vs. Non-fibrotic tissue was evaluated on Detection of atrial fibrosis (Area Under the Curve) (AUC 0.98, p=<0.05). The gain in eigenvalue dominance produced by ensemble alignment (∆RA) accurately detected atrial fibrosis in a simulated model, achieving an area under the curve of 0.98 and an accuracy of 1.
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