Graduation Year


Document Type




Degree Granting Department

Biomedical Engineering

Major Professor

Michael VanAuker, Ph.D.


Heart valves, Hemodynamics, Valvular stenosis, Porcine valves, Cross-linking agents, Pulse duplicator, Bayesian-learning networks


Aortic Valve Analysis and Area Prediction using Bayesian Modeling Miheer S. Ghotikar ABSTRACT Aortic valve stenosis affects approximately 5 out of every 10,000 people in the United States. [3] This disorder causes decrease in the aortic valve opening area increasing resistance to blood flow. Detection of early stages of valve malfunction is an important area of research to enable new treatments and develop strategies in order to delay degenerative progression. Analysis of relationship between valve properties and hemodynamic factors is critical to develop and validate these strategies. Porcine aortic valves are anatomically analogous to human aortic valves. Fixation agents modify the valves in such a manner to mimic increased leaflet stiffness due to early degeneration. In this study, porcine valves treated with glutaraldehyde, a cross-linking agent and ethanol, a dehydrating agent were used to alter leaflet material properties.

The hydraulic performance of ethanol and glutaraldehyde treated valves was compared to fresh valves using a programmable pulse duplicator that could simulate physiological conditions. Hydraulic conditions in the pulse duplicator were modified by varying mean flow rate and mean arterial pressure. Pressure drops across the aortic valve, flow rate and back pressure (mean arterial pressure) values were recorded at successive instants of time. Corresponding values of pressure gradient were measured, while aortic valve opening area was obtained from photographic data. Effects of glutaradehyde cross-linking and ethanol dehydration on the aortic valve area for different hydraulic conditions that emulated hemodynamic physiological conditions were analyzed and it was observed that glutaradehyde and ethanol fixation causes changes in aortic valve opening and closing patterns.

Next, relations between material properties, experimental conditions, and hydraulic measures of valve performance were studied using a Bayesian model approach. The primary hypothesis tested in this study was that a Bayesian network could be used to predict dynamic changes in the aortic valve area given the hemodynamic conditions. A Bayesian network encodes probabilistic relationships among variables of interest, also representing causal relationships between temporal antecedents and outcomes. A Learning Bayesian Network was constructed; direct acyclic graphs were drawn in GeNIe 2.0ʾ using an information theory dependency algorithm. Mutual Information was calculated between every set of parameters. Conditional probability tables and cut-sets were obtained from the data with the use of Matlabʾ.

A Bayesian model was built for predicting dynamic values of opening and closing area for fresh, ethanol fixed and glutaradehyde fixed aortic valves for a set of hemodynamic conditions. Separate models were made for opening and closing cycles. The models predicted aortic valve area for fresh, ethanol fixed and glutaraldehyde fixed valves. As per the results obtained from the model, it can be concluded that the Bayesian network works successfully with the performance of porcine valves in a pulse duplicator. Further work would include building the Bayesian network with additional parameters and patient data for predicting aortic valve area of patients with progressive stenosis. The important feature would be to predict valve degenration based on valve opening or closing pattern.