Project description
Atrial fibrillation (AF) exhibits highly complex spatial and temporal behaviours, including rotor formation, intermittent organisation, and fluctuating correlation structures. This project aims to develop and test quantitative metrics that capture these emergent properties of AF. Using simulated AF datasets, students will compute phase singularity lifetimes, rotor formation rates, spatial correlation lengths, recurrence metrics, and entropy-based measures of complexity. The relationship between these metrics and underlying substrate properties (e.g., fibrosis density, anisotropy, conduction velocity) will be explored. Outcome: A toolbox of emergent AF metrics with potential value for predicting AF stability and supporting mechanistic digital-twin modelling.
Assumed knowledge
Programming
Supervisors research focus
Computational cardiology, data science, signal processing, machine learning, biomedical engineering, biophysics
Note: You need to register interest in projects from different supervisors (not a number of projects with the one supervisor).
You must also contact each supervisor directly to discuss both the project details and your suitability to undertake the project.