Description:

The inverse problem of electrocardiography essentially requires one to predict what is happening on the heart surface of a patient (called EGMs), given their body surface potentials (called ECGs). If a good prediction can be made, then cardiologists can use these predictions to easily and efficiently plan heart surgeries.

We have developed mathematical models for predicting EGMs from ECGs. Vison transformer AI models, in particular Dino v2 (https://github.com/facebookresearch/dinov2) from Meta AI has been used to learn EGMs represented as heat maps from ECGs also represented as heat maps. We have got very good preliminary results. 

The aim of the project is to extend the AI model to handle complex arrhythias (heart problems).

Type:

Undergraduate

Outcome:

A generative/transformer AI model that can predict arrhythmias from ECGs to EGMs. In particular the source location of the arrhythmia.

Prerequisites

None

Specialisations

• Computer Systems Engineering
• Software Engineering

Categories

• AI & Machine learning

Supervisor

• Avinash Malik

Co-supervisor

• Mark Trew

Team

• Aaron Guo
• Josh Jeffers

Lab

Embedded Systems (405.760, Lab)