Friday, October 28, 2011
Hall 1-2 (San Jose Convention Center)
Blood pressure is directly related to the height, weight, and health of a subject. Discovering
a method to predict parameters in a model that can predict blood pressure for any person would
therefore aid in identifying which specific parameters need to be regulated to analyze
the health of a patient. The purpose of this project is to model how the
the autonomic nervous system responds to changes in position for a
subject during head up tilt (HUT) using heart rate and blood pressure data obtained from
(five healthy subjects). To do so we use a compartment model that use heart rate as an input to predict
blood pressure in the arteries and veins of the systemic circulation. HUT is modeled by accounting for gravitational
forces pooling blood in the legs, and the regulatory response is modeled by changing peripheral resistance, cardiac contractility, and vascular tone. Sensitivity analysis and subset selection was used to analyze what parameters that could be estimated given
the model and available data, and nonlinear optimization was used to estimate values for these parameters. Several
methods was employed including the Levenberg-Marqardt gradient based method and Kalman Filtering.
a method to predict parameters in a model that can predict blood pressure for any person would
therefore aid in identifying which specific parameters need to be regulated to analyze
the health of a patient. The purpose of this project is to model how the
the autonomic nervous system responds to changes in position for a
subject during head up tilt (HUT) using heart rate and blood pressure data obtained from
(five healthy subjects). To do so we use a compartment model that use heart rate as an input to predict
blood pressure in the arteries and veins of the systemic circulation. HUT is modeled by accounting for gravitational
forces pooling blood in the legs, and the regulatory response is modeled by changing peripheral resistance, cardiac contractility, and vascular tone. Sensitivity analysis and subset selection was used to analyze what parameters that could be estimated given
the model and available data, and nonlinear optimization was used to estimate values for these parameters. Several
methods was employed including the Levenberg-Marqardt gradient based method and Kalman Filtering.