Many physiological processes of interest in clinical applications involve pressures, flows, and volumes and are therefore well suited to being modeled by circuit analogs. These circuit models, when fitted to data, can provide insight into the underlying physiology and inform diagnoses. This project will investigate the application of such models to clinical data obtained by capnography, the measurement of CO2 partial pressure during expiration, in order to discern respiratory parameters associated with various diseases. A particular focus will be exploring how circuit topology and complexity impact the consistency of parameter estimation and success at disease classification. An eventual goal is to automatically generate the most useful low-complexity models to fit measured data.

I am participating in SuperUROP to apply what I’ve learned in my coursework, in particular, Course 6.011 (Signals, Systems, and Inference) to a year-long biomedical signal processing research project. I hope to learn more about the modeling of physiological systems, and I’m excited to be part of a research community working towards improving care in ICUs.