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Unipolar Electrogram Eigenvalue Distribution Analysis for the Identification of Atrial Fibrosis

文献类型： 会议论文

第一作者： Jennifer Riccio

作者： Jennifer Riccio 1 ; Sara Rocher 2 ; Laura Martinez-Mateu 2 ; Alejandro Alcaine 3 ; Javier Saiz 2 ; Juan Pablo Martinez 1 ; Pablo Laguna 1 ;

作者机构： 1.BSICoS, I3A, IIS Aragon, Universidad de Zaragoza

2.Ci2B, Universitat Politecnica de Valencia

3.CIBER en Bioingenieria, Biomateriales y Nanomedicina (CIBER-BBN)

会议名称： Computing in Cardiology Conference

主办单位：

页码： 1041-1044

摘要： 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.

分类号： tp399-53

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