79664 20190819103726.0 doi 10.1007/s11517-017-1696-9 sideral 101672 ART-2017-101672 eng (orcid)0000-0003-4273-5403 Sánchez, C. Sensitivity analysis of ventricular activation and electrocardiogram in tailored models of heart-failure patients 2017 Access copy available to the general public Unrestricted Cardiac 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. info:eu-repo/semantics/openAccess All rights reserved http://www.europeana.eu/rights/rr-f/ 1.971 2017 MATHEMATICAL & COMPUTATIONAL BIOLOGY 19 / 59 = 0.322 2017 Q2 T1 MEDICAL INFORMATICS 14 / 25 = 0.56 2017 Q3 T2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 52 / 105 = 0.495 2017 Q2 T2 ENGINEERING, BIOMEDICAL 41 / 78 = 0.526 2017 Q3 T2 0.661 2017 Computer Science Applications 2017 Q2 Biomedical Engineering 2017 Q2 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion D’Ambrosio, G. Maffessanti, F. Caiani, E.G. Prinzen, F.W. Krause, R. Auricchio, A. Potse, M. 56, 3 (2017), 491-504 Med. biol. eng. comput. MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 0140-0118 432865 http://zaguan.unizar.es/record/79664/files/texto_completo.pdf Postprint 57321 http://zaguan.unizar.es/record/79664/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:79664 articulos driver 2019-08-19-09:42:41 ARTICLE