Respiration involves volumes, flows, pressures and concentrations, and so is amenable to modeling by circuit analogs. These physiologically meaningful models can be fitted to routinely measured but insufficiently exploited clinical data such as the capnogram, which is the waveform of CO2 concentration in exhaled breath, recorded as a function of time or of exhaled volume. The resulting estimated model parameters can aid screening, diagnosis and monitoring. This project will extend our current capnogram models to novel clinical settings. In particular, we aim to better understand the respiratory dynamics of ventilated patients, using appropriate models and data to estimate parameters of clinical interest, such as alveolar CO2 concentration, lung compliance, and unperfused deadspace volume.

I’ m looking forward to participating in SuperUROP to gain more experience in biomedical research and refine my understanding of modeling physiological systems. I would like to apply what I’ ve learned from 6.011 (Signals, Systems, and Inference), as well as from my previous work on this project, to contribute to a research community working to improve ventilatory management in intensive care units, especially in light of recent events.