Research Projects
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Featured Projects
Renewal theory of cardiac fibrillation
Cardiac fibrillation is a condition characterised by irregular heart rhythm. It can exist in two forms: atrial fibrillation (AF) and ventricular fibrillation (VF), respectively the most common heart rhythm disorder and leading cause of sudden death in the world. These conditions arise due to the presence of spinning vortices of electricity in the heart, leading to spiral defect chaos. Renewal theory mathematically models the rate at which these spirals form and die, which captures the macroscale system properties and underlying physiology of the heart.
Read about some of this work:
1. Renewal theory as a quantitative framework
2. Using Renewal theory to develop governing equations in AF
3. Using Renewal theory to develop governing equations in VF
Understanding the spontaneous termination of cardiac fibrillation
An interesting feature of cardiac fibrillation is that it has the ability to self-terminate abruptly, without needing any intervention. This can happen even in episodes of cardiac fibrillation that have persisted for weeks or months! Understanding which episodes of fibrillation will sustain versus those that self-terminate is highly clinically useful, as this means we can differentiate patients who need treatment from those who will have fibrillation that will resolve on its own. This helps avoid unnecessary intervention and more effectively allocates medical resources. To study this, we are looking at various signal features and concepts from transient chaos to differentiate/predict these two scenarios.
Calibration of computational models using clinically derived signal metrics
In collaboration with Dr Caroline Roney (Personalised Cardiac Modelling Lab, Queen Mary University of London) and Professor Steven Niederer (Cardiac Electromechanics Research Group, Imperial College London), this project explores whether model parameters in computational simulations of the heart can be calibrated using metrics derived from clinically collected patient data, which measure emergent electrical dynamics.
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Watch some video explainers
Don't feel like reading? Watch some short videos explaining some of the research work instead