Biomedical Engineering: Advanced Biomedical Systems

Uses the study of lung mechanics as a vehicle for teaching a range of mathematical modeling and data analysis methods central to the study of physiological systems. Students will gain a detailed understanding of how the lung works as a mechanical system and various diseases that affect mechanical function. At the same time, they will learn about applications of a range of mathematical and signal processing techniques. Prerequisite: Graduate standing or Instructor permission.

Open to degree and PACE students

Remote learning course that uses the study of lung mechanics as a vehicle for teaching a range of mathematical modeling and data analysis methods central to the study of physiological systems. Students will gain a detailed understanding of how the lung works as a mechanical system and various diseases that affect mechanical function. At the same time, they will learn about applications of compartment modeling, the physics of biological tissue, model fitting techniques that include multiple linear regression and recursive linear regression, linear systems theory, the theory and application of impedance, and nonlinear systems theory. An overarching goal of the course is to show how mathematical models of a physiological system can progress from the simple through to the complex as the need arises.

The textbook for the course is Lung Mechanics: An Inverse Modeling Approach by Jason H.T. Bates, published by Cambridge University Press in 2009. Students will be assigned readings and pre-recorded PowerPoint slide presentations each week, supplemented by key papers from the archival scientific literature. Students are expected to study the designated course materials each week and to complete a weekly assignment that must be handed by a specified deadline. Students are expected to attend a 2 hr weekly Zoom/Teams session with the instructor at which they will discuss the material and ask questions. Eight to 10 hours of additional study each week is also expected.

Grade Contribution: Weekly assignments (85%), Participation (15%)