Unipolar Electrogram Eigenvalue Distribution Analysis for the Identification of Atrial Fibrosis

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. Performance of each map in detecting fibrosis has been evaluated using receiver operating characteristic curves and detection accuracy. Best results achieve an area under the curve (AUC) of 0.98 and an accuracy (ACC) of 1 when we use as marker the gain in eigenvalue dominance produced by the ensemble alignment.