Generalized polynomial chaos-based uncertainty quantification and propagation in multi-scale modeling of cardiac electrophysiology.
Uncertainty and physiological variability are ubiquitous in cardiac electrical signaling. It is important to address the uncertainty and variability in cardiac modeling to provide reliable and realistic predictions of heart function, thus ensuring trustworthy computer-aided medical decision-making and treatment planning. Statistical techniques such as Monte Carlo (MC) simulations have been applied to uncertainty quantification and propagation in cardiac modeling. However, MC simulation-based methods are computationally prohibitive for complex cardiac models with a great number of parameters and governing equations. In this paper, we propose to use the Generalized Polynomial Chaos (gPC) expansion in combination with Galerkin projection to analytically quantify parametric uncertainty in ion channel models of mouse ventricular cell, and further propagate the uncertainty across different organizational levels of cell and tissue. To identify the most significant parametric uncertainty in cardiac ion channel and cell models, variance decomposition-based sensitivity analysis was first performed. Following this, gPC was integrated with deterministic cardiac models to propagate uncertainty through ion current, ventricular cell, 1D cable, and 2D tissue to account for the stochasticity and cell-to-cell variability. As compared to MC, the gPC in this work shows the superior performance in terms of computational efficiency. In addition, the gPC models can provide a measure of confidence in model predictions, which can improve the reliability of computer simulations of cardiac electrophysiology for clinical applications.
['Hu|Zhiyong|Z|', 'Du|Dongping|D|', 'Du|Yuncheng|Y|']
D000818:Animals D019295:Computational Biology D003198:Computer Simulation D004594:Electrophysiology D006352:Heart Ventricles D007477:Ions D051379:Mice D008955:Models, Cardiovascular D015233:Models, Statistical D009010:Monte Carlo Method D032342:Muscle Cells D017711:Nonlinear Dynamics D016011:Normal Distribution D011336:Probability D015203:Reproducibility of Results D013269:Stochastic Processes D035501:Uncertainty D016276:Ventricular Function
Cardiac electrophysiology Sensitivity analysis Mouse ventricular myocyte Stochastic model Uncertainty propagation