Subset selection

Short-term cardiovascular responses to head-up tilt (HUT) involve complex cardiovascular regulation in order to maintain blood pressure at homoeostatic levels. This manuscript presents a patient-specific model that uses heart rate as an input to fit the dynamic changes in arterial blood pressure data during HUT. The model contains five compartments representing arteries and veins in the upper and lower body of the systemic circulation, as well as the left ventricle facilitating pumping of the heart.

Short-term cardiovascular responses to head-up tilt (HUT) involve complex cardiovascular regulation in order to maintain blood pressure at homoeostatic levels. This manuscript presents a patient-specific model that uses heart rate as an input to fit the dynamic changes in arterial blood pressure data during HUT. The model contains five compartments representing arteries and veins in the upper and lower body of the systemic circulation, as well as the left ventricle facilitating pumping of the heart.

This study develops a coupled cardiovascular-respiratory model that predicts cerebral blood flow velocity (CBFV), arterial blood pressure, end-tidal CO2, and ejection fraction for a patient with congestive heart failure. The model is a lumped parameter model giving rise to a system of ordinary differential equations. We use sensitivity analysis and subset selection to identify a set of model parameters that can be estimated given the patient data. Gradient based nonlinear optimization is used to estimate the subset of parameters.

Numerous mathematical models have been proposed for prediction of baroreflex regulation of heart rate. Most models have been designed to provide qualitative predictions of the phenomena, though some recent models have been developed to predict observed data. In this study we show how sensitivity and correlation analysis can be used for model reduction and for obtaining a set of identifiable parameters that can be estimated reliably given a model and an associated set of data. We show that the model developed by Bugenhagen et al.

Mathematical models have long been used for prediction of dynamics in biological systems. Recently, several efforts have been made to render these models patient specific. One way to do so is to employ techniques to estimate parameters that enable model based prediction of observed quantities. Knowledge of variation in parameters within and between groups of subjects have potential to provide insight into biological function. Often it is not possible to estimate all parameters in a given model, in particular if the model is complex and the data is sparse.