Nov. 15: "Modeling the Short-Term Effect of Atrial Fibrillation on Hemodynamic Variables"
Date: Wednesday, November 15, 2023
Time: 9:30-10:30AM
Location: Health Science Technology Building (HST), Forum Room 101
This event features postdoc Sanmi Adeodu who will talk about "Modeling the Short-Term Effect of Atrial Fibrillation on Hemodynamic Variables", as part of the Lehigh University Chemical and Biomolecular Engineering's Fall 2023 Colloquium Seminar Series.


Oluwasanmi Adeodu, Michelle Gee, Babak Mahmoudi, Rajanikanth Vadigepalli, Mayuresh Kothare
Atrial fibrillation (AF) remains the leading cardiac cause of stroke in the United States. An elevated and highly irregular heart rate is the trademark feature of AF and stems from the conduction of abnormal electrical signals from the atria to the ventricles. However, the impact of AF on other hemodynamic variables is not as well established since hypertension, heart failure and other cardiac diseases are often confounding factors. Our goal is to clarify the short-term impact of stand-alone AF on hemodynamic quantities using a lumped parameter approach. We developed a paroxysmal AF model by making three modifications to a computational model of the human cardiovascular-baroreflex system. Based on the relative constancy of the coefficient of variation of the change in successive heart periods (observed in the MIT-BIH AF dataset), we recast instantaneous heart period as a bounded stochastic variable. In addition, we redefined the healthy left atrium as a pulsating compartment that boosts ventricular intake during late diastole. This contribution to mitral flow is muted during AF. AF has also been linked to the suppression of parasympathetic drive and a simultaneous enhancement of sympathetic activity. Thus, our third modification involves the development of a valid baroreflex sensitivity metric to quantify the extent of baroreflex impairment. Our model predictions of the changes in stroke volume and mean arterial pressure during AF episodes match published paroxysmal AF data. Insights obtained from this model will aid the in-silico assessment of potential neuromodulation strategies for AF suppression and also increase the specificity of AF detection algorithms.

About the Speaker

Dr. Sanmi Adeodu is a current post-doc in the Process Modeling and Control group under Professor Mayuresh Kothare. He earned his BSc in Chemical Engineering from Obafemi Awolowo University, Nigeria and a MSc at University College London where his research involved characterizing zeolites using chemisorption. He switched his research focus to Advanced Process Control during his PhD at Illinois Institute of Technology where he worked on the optimization and control of grid-scale energy storage systems using Economic MPC. Now, Sanmi is applying ideas in optimal control to design control systems for neuromodulation of the human cardiac system to improve health outcomes in atrial fibrillation and other cardiovascular diseases.