000079664 001__ 79664
000079664 005__ 20190819103726.0
000079664 0247_ $$2doi$$a10.1007/s11517-017-1696-9
000079664 0248_ $$2sideral$$a101672
000079664 037__ $$aART-2017-101672
000079664 041__ $$aeng
000079664 100__ $$0(orcid)0000-0003-4273-5403$$aSánchez, C.
000079664 245__ $$aSensitivity analysis of ventricular activation and electrocardiogram in tailored models of heart-failure patients
000079664 260__ $$c2017
000079664 5060_ $$aAccess copy available to the general public$$fUnrestricted
000079664 5203_ $$aCardiac resynchronization therapy is not effective in a variable proportion of heart failure patients. An accurate knowledge of each patient’s electroanatomical features could be helpful to determine the most appropriate treatment. The goal of this study was to analyze and quantify the sensitivity of left ventricular (LV) activation and the electrocardiogram (ECG) to changes in 39 parameters used to tune realistic anatomical-electrophysiological models of the heart. Electrical activity in the ventricles was simulated using a reaction-diffusion equation. To simulate cellular electrophysiology, the Ten Tusscher-Panfilov 2006 model was used. Intracardiac electrograms and 12-lead ECGs were computed by solving the bidomain equation. Parameters showing the highest sensitivity values were similar in the six patients studied. QRS complex and LV activation times were modulated by the sodium current, the cell surface-to-volume ratio in the LV, and tissue conductivities. The T-wave was modulated by the calcium and rectifier-potassium currents, and the cell surface-to-volume ratio in both ventricles. We conclude that homogeneous changes in ionic currents entail similar effects in all ECG leads, whereas the effects of changes in tissue properties show larger inter-lead variability. The effects of parameter variations are highly consistent between patients and most of the model tuning could be performed with only ~10 parameters.
000079664 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000079664 590__ $$a1.971$$b2017
000079664 591__ $$aMATHEMATICAL & COMPUTATIONAL BIOLOGY$$b19 / 59 = 0.322$$c2017$$dQ2$$eT1
000079664 591__ $$aMEDICAL INFORMATICS$$b14 / 25 = 0.56$$c2017$$dQ3$$eT2
000079664 591__ $$aCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS$$b52 / 105 = 0.495$$c2017$$dQ2$$eT2
000079664 591__ $$aENGINEERING, BIOMEDICAL$$b41 / 78 = 0.526$$c2017$$dQ3$$eT2
000079664 592__ $$a0.661$$b2017
000079664 593__ $$aComputer Science Applications$$c2017$$dQ2
000079664 593__ $$aBiomedical Engineering$$c2017$$dQ2
000079664 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000079664 700__ $$aD’Ambrosio, G.
000079664 700__ $$aMaffessanti, F.
000079664 700__ $$aCaiani, E.G.
000079664 700__ $$aPrinzen, F.W.
000079664 700__ $$aKrause, R.
000079664 700__ $$aAuricchio, A.
000079664 700__ $$aPotse, M.
000079664 773__ $$g56, 3 (2017), 491-504$$pMed. biol. eng. comput.$$tMEDICAL & BIOLOGICAL ENGINEERING & COMPUTING$$x0140-0118
000079664 8564_ $$s432865$$uhttps://zaguan.unizar.es/record/79664/files/texto_completo.pdf$$yPostprint
000079664 8564_ $$s57321$$uhttps://zaguan.unizar.es/record/79664/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000079664 909CO $$ooai:zaguan.unizar.es:79664$$particulos$$pdriver
000079664 951__ $$a2019-08-19-09:42:41
000079664 980__ $$aARTICLE