Feddo Kirkels

Uncertainty Quantification of Cardiac Properties | 99 18. Voigt JU, Pedrizzetti G, Lysyansky P, et al. Definitions for a common standard for 2D speckle tracking echocardiography: consensus document of the EACVI/ASE/Industry Task Force to standardize deformation imaging. Eur Heart J Cardiovasc Imaging. 2015;16(1):1–11. 19. Arts T, Delhaas T, Bovendeerd P, Verbeek X, Prinzen FW. Adaptation to mechanical load determines shape and properties of heart and circulation: the CircAdapt model. Am J Physiol Heart Circ. 2005;288(4):H1943–54. 20. Lumens J, Delhaas T, Kirn B, Arts T. Three-wall segment (TriSeg) model describing mechanics and hemodynamics of ventricular interaction. Ann Biomed Eng. 2009;37(11):2234–55. 21. Walmsley J, Arts T, Derval N, et al. Fast Simulation of Mechanical Heterogeneity in the Electrically Asynchronous Heart Using the MultiPatch Module. PLoS Comput Biol. 2015;11:1–23. 22. Freedman D, Diaconis P. On the histogram as a density estimator:L 2 theory. Zeitschrift fur Wahrscheinlichkeitstheorie und Verwandte Gebiete. 1981;57(4):453–76. 23. Corrado C, Gerbeau JF, Moireau P. Identification of weakly coupled multiphysics problems. Application to the inverse problem of electrocardiography. J Comput Phys. 2015;283:271–98. 24. Zenker S. Parallel particle filters for online identification of mechanistic mathematical models of physiology from monitoring data: performance and real-time scalability in simulation scenarios. J Clin Monit Comput. 2010;24(4):319–33. 25. Dhamala J, Bajracharya P, Arevalo HJ, et al. Embedding high-dimensional Bayesian optimization via generative modeling: Parameter personalization of cardiac electrophysiological models. Med Image Anal. 2020;62:101670. 26. Coveney S, Clayton RH. Fitting two human atrial cell models to experimental data using Bayesian history matching. Prog Biophys Mol Biol. 2018;139:43–58. 27. Daly AC, Cooper J, Gavaghan DJ, Holmes C. Comparing two sequential Monte Carlo samplers for exact and approximate Bayesian inference on biological models. J R Soc Interface. 2017;14(134):20170340. 28. Camps J, Lawson B, Drovandi C, et al. Inference of ventricular activation properties from non-invasive electrocardiography. Med Image Anal. 2021;73:102–43. 29. Davies V, Noè U, Lazarus A, et al. Fast Parameter Inference in a Biomechanical Model of the Left Ventricle by Using Statistical Emulation. J R Stat Soc Ser C Appl Stat. 2019;68(5):1555–76. 30. Dhamala J, Arevalo HJ, Sapp J, et al. Spatially Adaptive Multi-Scale Optimization for Local Parameter Estimation in Cardiac Electrophysiology. IEEE Trans Med Imaging. 2017;36(9):1966–78. 31. Paun LM, Colebank M, Qureshi U, Olufsen M, Hill N, Husmeier D. MCMC with delayed acceptance using a surrogate model with an application to cardiovascular fluid dynamics. Proceedings of the International Conference on Statistics: Theory and Applications. 2019. 32. Lei CL, Ghosh S, Whittaker DG, et al. Considering discrepancy when calibrating a mechanistic electrophysiology model. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2020;378(2173):20190349. 33. Arts T, Lumens J, Kroon W, Delhaas T. Control of Whole Heart Geometry by Intramyocardial Mechano-Feedback: A Model Study. PLoS Comput Biol. 2012;8(2):e1002369. 34. van Opbergen CJM, Noorman M, Pfenniger A, et al. Plakophilin-2 Haploinsufficiency Causes Calcium Handling Deficits and Modulates the Cardiac Response Towards Stress. Int J Mol Sci. 2019;20(17):4076. 5

RkJQdWJsaXNoZXIy MTk4NDMw