000061445 001__ 61445
000061445 005__ 20200221144312.0
000061445 0247_ $$2doi$$a10.1016/j.dsp.2016.04.002
000061445 0248_ $$2sideral$$a94144
000061445 037__ $$aART-2016-94144
000061445 041__ $$aeng
000061445 100__ $$0(orcid)0000-0002-0166-2837$$aAlcaine, Alejandro
000061445 245__ $$aSpatiotemporal Model-Based Estimation of High-Density Atrial Fibrillation Activation Map
000061445 260__ $$c2016
000061445 5060_ $$aAccess copy available to the general public$$fUnrestricted
000061445 5203_ $$aExamination of activation maps using multi-electrode array (MEA) sensors can help to understand the mechanisms underlying atrial fibrillation (AF). Classically, creation of activation maps starts with detection of local activation times (LAT) based on recorded unipolar electrograms. LAT detection has a limited robustness and accuracy, and generally requires manual edition. In general, LAT detection ignores spatiotemporal information of activation embedded in the relation between electrode signals on the MEA mapping sensor. In this work, a unified approach to construct activation maps by simultaneous analysis of activation patterns from overlapping clusters of MEA electrodes is proposed. An activation model fits on the measured data by iterative optimization of the model parameters based on a cost function. The accuracy of the estimated activation maps was evaluated by comparison with audited maps created by expertelectrophysiologists during sinus rhythm (SR) and AF. During SR recordings, 25 activation maps (3100 LATs) were automatically determined resulting in an average LAT estimation error of -0.66 ±2.00msand a correlation of ¿s=0.98compared to the expert reference. During AF recordings (235 maps, 28226 LATs), the estimation error was -0.83 ±6.02mswith only a slightly lower correlation (¿s=0.93). In conclusion, complex spatial activation patterns can be decomposed into local activation patterns derived from fitting an activation model, allowing the creation of smooth and comprehensive high-density activation maps.
000061445 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T96$$9info:eu-repo/grantAgreement/ES/ISCIII/CIBER-BBN$$9info:eu-repo/grantAgreement/ES/MINECO/BES-2011-046644$$9info:eu-repo/grantAgreement/ES/MINECO/EEBB-I-13-06613$$9info:eu-repo/grantAgreement/ES/MINECO/TEC2013-42140-R
000061445 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000061445 590__ $$a2.337$$b2016
000061445 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b98 / 260 = 0.377$$c2016$$dQ2$$eT2
000061445 592__ $$a0.597$$b2016
000061445 593__ $$aSignal Processing$$c2016$$dQ2
000061445 593__ $$aElectrical and Electronic Engineering$$c2016$$dQ2
000061445 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000061445 700__ $$ade Groot, N.M.
000061445 700__ $$0(orcid)0000-0003-3434-9254$$aLaguna, Pablo$$uUniversidad de Zaragoza
000061445 700__ $$0(orcid)0000-0002-7503-3339$$aMartínez, J.P.$$uUniversidad de Zaragoza
000061445 700__ $$aHouben, R.P.
000061445 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac.
000061445 773__ $$g54 (2016), 64-74$$pDigit. signal process.$$tDIGITAL SIGNAL PROCESSING$$x1051-2004
000061445 8564_ $$s12548567$$uhttps://zaguan.unizar.es/record/61445/files/texto_completo.pdf$$yPostprint
000061445 8564_ $$s43154$$uhttps://zaguan.unizar.es/record/61445/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000061445 909CO $$ooai:zaguan.unizar.es:61445$$particulos$$pdriver
000061445 951__ $$a2020-02-21-13:37:31
000061445 980__ $$aARTICLE