000099442 001__ 99442
000099442 005__ 20230914083818.0
000099442 0247_ $$2doi$$a10.22489/CinC.2020.434
000099442 0248_ $$2sideral$$a123038
000099442 037__ $$aART-2020-123038
000099442 041__ $$aeng
000099442 100__ $$0(orcid)0000-0002-6264-4229$$aRiccio, Jennifer$$uUniversidad de Zaragoza
000099442 245__ $$aUnipolar Electrogram Eigenvalue Distribution Analysis for the Identification of Atrial Fibrosis
000099442 260__ $$c2020
000099442 5060_ $$aAccess copy available to the general public$$fUnrestricted
000099442 5203_ $$aAtrial 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.
000099442 536__ $$9info:eu-repo/grantAgreement/ES/MICINN/PID2019-104881RB-I00$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 766082-MY-ATRIA$$9info:eu-repo/grantAgreement/EC/H2020/766082/EU/MultidisciplinarY training network for ATrial fibRillation monItoring, treAtment and progression/MY-ATRIA$$9info:eu-repo/grantAgreement/ES/DGA-FSE/T39-20R-BSICoS group
000099442 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000099442 592__ $$a0.257$$b2020
000099442 593__ $$aComputer Science (miscellaneous)$$c2020
000099442 593__ $$aCardiology and Cardiovascular Medicine$$c2020
000099442 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000099442 700__ $$aRocher Ventura, Sara
000099442 700__ $$aMartínez Mateu, Laura
000099442 700__ $$0(orcid)0000-0002-0166-2837$$aAlcaine Otín, Alejandro
000099442 700__ $$aSáiz Rodríguez, Javier
000099442 700__ $$0(orcid)0000-0002-7503-3339$$aMartínez Cortés, Juan Pablo$$uUniversidad de Zaragoza
000099442 700__ $$0(orcid)0000-0003-3434-9254$$aLaguna Lasaosa, Pablo$$uUniversidad de Zaragoza
000099442 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac.
000099442 773__ $$g47 (2020), [ 4 pp.]$$pComput. cardiol.$$tComputing in Cardiology$$x2325-8861
000099442 8564_ $$s883113$$uhttps://zaguan.unizar.es/record/99442/files/texto_completo.pdf$$yVersión publicada
000099442 8564_ $$s2692825$$uhttps://zaguan.unizar.es/record/99442/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000099442 909CO $$ooai:zaguan.unizar.es:99442$$particulos$$pdriver
000099442 951__ $$a2023-09-13-15:32:51
000099442 980__ $$aARTICLE