Estimation and identification of parameters in a lumpedcerebrovascular model - Scott R. Pope; Laura M. Ellwein; Cheryl L. Zapata; Vera Novak; C. T. Kelley; Mette S. Olufsen (Mathematical Biosciences and Engineering)
This study shows how sensitivity analysis and subset selection can be employedin a cardiovascular model to estimate total systemic resistance,cerebrovascular resistance, arterial compliance, and time for peak systolicventricular pressure for healthy young and elderly subjects. These quantitiesare parameters in a simple lumped parameter model that predicts pressure andflow in the systemic circulation. The model is combined with experimentalmeasurements of blood flow velocity from the middle cerebral artery andarterial finger blood pressure. To estimate the model parameters we usenonlinear optimization combined with sensitivity analysis and subset selection.Sensitivity analysis allows us to rank model parameters from the most to theleast sensitive with respect to the output states (cerebral blood flowvelocity and arterial blood pressure). Subset selection allows us to identifya set of independent candidate parameters that can be estimated given limiteddata. Analyses of output from both methods allow us to identify fiveindependent sensitive parameters that can be estimated given the data.Results show that with the advance of age total systemic and cerebralresistances increase, that time for peak systolic ventricular pressure isincreases, and that arterial compliance is reduced. Thus, the method discussedin this study provides a new methodology to extract clinical markers thatcannot easily be assessed noninvasively.