000077127 001__ 77127
000077127 005__ 20200108100343.0
000077127 0247_ $$2doi$$a10.1109/JBHI.2018.2805773
000077127 0248_ $$2sideral$$a106144
000077127 037__ $$aART-2018-106144
000077127 041__ $$aeng
000077127 100__ $$aLuengo, David
000077127 245__ $$aHierarchical algorithms for causality retrieval in atrial fibrillation intracavitary electrograms
000077127 260__ $$c2018
000077127 5060_ $$aAccess copy available to the general public$$fUnrestricted
000077127 5203_ $$aMulti-channel intracavitary electrograms (EGMs), are acquired at the electrophysiology laboratory to guide radio frequency catheter ablation of patients suffering from atrial fibrillation (AF). These EGMs are used by cardiologists to determine candidate areas for ablation (e.g., areas corresponding to high dominant frequencies or complex fractionated electrograms). In this paper, we introduce two hierarchical algorithms to retrieve the causal interactions among these multiple EGMs. Both algorithms are based on Granger causality, but other causality measures can be easily incorporated. In both cases, they start by selecting a root node, but they differ on the way in which they explore the set of signals to determine their cause-effect relationships: either testing the full set of unexplored signals (GS-CaRe) or performing a local search only among the set of neighbor EGMs (LS-CaRe). The ensuing causal model provides important information about the propagation of the electrical signals inside the atria, uncovering wavefronts and activation patterns that can guide cardiologists towards candidate areas for catheter ablation. Numerical experiments, on both synthetic signals and annotated real-world signals, show the good performance of the two proposed approaches.
000077127 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/TEC2015-64835-C3-3-R$$9info:eu-repo/grantAgreement/ES/MINECO/TEC2015-69868-C2-1-R
000077127 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000077127 590__ $$a4.217$$b2018
000077127 591__ $$aCOMPUTER SCIENCE, INFORMATION SYSTEMS$$b19 / 155 = 0.123$$c2018$$dQ1$$eT1
000077127 591__ $$aMATHEMATICAL & COMPUTATIONAL BIOLOGY$$b5 / 59 = 0.085$$c2018$$dQ1$$eT1
000077127 591__ $$aMEDICAL INFORMATICS$$b4 / 26 = 0.154$$c2018$$dQ1$$eT1
000077127 591__ $$aCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS$$b16 / 106 = 0.151$$c2018$$dQ1$$eT1
000077127 592__ $$a1.122$$b2018
000077127 593__ $$aBiotechnology$$c2018$$dQ1
000077127 593__ $$aHealth Information Management$$c2018$$dQ1
000077127 593__ $$aElectrical and Electronic Engineering$$c2018$$dQ1
000077127 593__ $$aComputer Science Applications$$c2018$$dQ1
000077127 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000077127 700__ $$aRíos-Muñoz, Gonzalo Ricardo
000077127 700__ $$aElvira, Víctor
000077127 700__ $$aSánchez, Carlos
000077127 700__ $$aArtés-Rodríguez, Antonio
000077127 773__ $$g23, 1 (2018), 143-155$$pIEEE j. biomed. health inform.$$tIEEE journal of biomedical and health informatics$$x2168-2194
000077127 8564_ $$s808864$$uhttps://zaguan.unizar.es/record/77127/files/texto_completo.pdf$$yPostprint
000077127 8564_ $$s143836$$uhttps://zaguan.unizar.es/record/77127/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000077127 909CO $$ooai:zaguan.unizar.es:77127$$particulos$$pdriver
000077127 951__ $$a2020-01-08-09:29:02
000077127 980__ $$aARTICLE