Predicting Coma Recovery Using Massive Neurophysiology Datasets

  • Project Year: 2025-26
  • Departments Represented: MEng
  • Industry/Track: Health & Wellbeing

Clinicians don’t have a universal method to predict coma recovery, leading to subjective predictions. Our team is developing machine learning models that objectify clinician decision-making and produce a prediction of a patient’s mortality, saving lives and resources. We are leveraging an EEG (brain signal) dataset of 1,000+ patients across multiple countries, combining models trained at scale on high performance computing clusters.

  • Team: Michael Brown [BIOE], Sandra Trajkovski [BIOE], Ahmed Mostafa [EECS], Keaton Lee [EECS], Alexander de Vet [ME]