Supervisors

Suitable for

Abstract

Prerequisites: Computational Medicine (recommended)

 

Background

• Hypertrophic cardiomyopathy (HCM) is a genetic heart disease and a leading cause of sudden cardiac death in the young. Identifying patients at high risk of lethal arrhythmias is crucial for improving patient outcomes. Electrocardiography (ECG) is commonly used in HCM to assess electrical activity of the heart, which may reveal markers of arrhythmic risk. However, the fundamental limits of what electrical activity can be revealed by the ECG remain incompletely understood.

Focus

• In the project, the student will investigate how different cardiac electrical patterns manifest as different ECG signatures using sensitivity analysis and computer modelling, to determine the main factors underlying 12-lead ECG signals. The student can then apply machine learning techniques to their ECG dataset and computer models, to build predictive models of cardiac electrical activity from the ECG. If successful, the student will then apply their methods to a clinical dataset of 12-lead ECGs, to better characterise the functional disease substrate of HCM patients.

 

Method

Electrocardiogram phenotypes in hypertrophic cardiomyopathy caused by distinct mechanisms: apico-basal repolarization gradients vs. Purkinje-myocardial coupling abnormalities. https://doi.org/10.1093/europace/euy226

Simulation-based digital twinning of activation and repolarisation sequences from the ECG across healthy and diseased hearts. https://doi.org/10.1016/j.compbiomed.2025.111222