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The arterial system dynamically loads the heart through changes in arterialcompliance. The pressure-volume relation of arteries is known to be nonlinear,but arterial compliance is often modeled as a constant value, due to ease ofestimation and interpretation. Incorporating nonlinear arterial complianceaffords insight into the continuous variations of arterial compliance in acardiac cycle and its effects on the heart, as the arterial system is coupledwith the left ventricle. We recently proposed a method for estimating nonlinearcompliance parameters that yielded good results under various vasoactivestates. This study examines the performance of the proposed method byquantifying the uncertainty of the method in the presence of noise andpropagating the uncertainty through the system model to analyze its effects onmodel predictions. Kernel density estimation used within a bootstrap MonteCarlo simulation showed the method to be stable for various vasoactive states.

Propagation of Uncertainty and Analysis of Signal-to-Noise in Nonlinear Compliance Estimations of an Arterial System Model