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Unit of study_

BMET5934: Biomedical Machine Learning

Designing artificial intelligence (AI) based systems for solving real world problems is about finding an appropriate AI tool for the task at hand. This unit aims to provide students with the opportunity to work in small groups (3-5 students per group) and design and implement an AI system that solves a real-world biomedical problem. Students will work with large database of multi-sensor biological signals from a public data source such as M.I.T Physionet or National Sleep Research Resource and design AI systems predicting desired biomedical outcomes. For example, the groups may design a system for automatic sleep staging of human sleep using electroencephalogram signals. The unit will emphasise using signal processing/machine learning tools to find practical and effective solutions to the posed biomedical problem.

Code BMET5934
Academic unit Biomedical Engineering
Credit points 6
Assumed knowledge:
BMET2901/9901 or equivalent, and (BMET2925 or BMET9925), and (BMET3997 or BMET9997 or ELEC3305 or ELEC9305)

At the completion of this unit, you should be able to:

  • LO1. Present written reports and make presentations to communicate technical and often complex material in clear and concise terms for a specific target audience.
  • LO2. Develop the ability to work in an interdisciplinary team effectively and efficiently by assuming clearly defined roles and responsibilities and then interacting in a constructive manner with the group by both contributing and evaluating others' viewpoints in a project where devices and software tools are deployed in a health environment.
  • LO3. Conceive and design an innovative health software application
  • LO4. Select signal processing methods on biological signals and appropriate machine learning algorithms to achieve required outcomes.
  • LO5. Explain what physiological signals are and how they are measured. Show proficiency in using state of the art tools and methods to analyse sensing data.
  • LO6. Apply appropriate signal processing and machine learning methods to achieve a practical solution to a real world biomedical problem

Unit outlines

Unit outlines will be available 2 weeks before the first day of teaching for the relevant session.