Investigation of cardiac phenotypes in cardiovascular disease using
Lead Research Organisation:
University of Oxford
Department Name: Computer Science
Abstract
This project falls within the EPSRC Healthcare Technologies theme.
The project will investigate cardiac phenotypes in cardiovascular disease using a combination of machine learning, signal processing and computer modelling. Experimental and clinical data such as the electrocardiogram and cardiac magnetic resonance will be analysis using signal and image processing techniques to quantify key properties for characterisation of the disease effects on the heart of patients affected by specifci disease conditions. Anatomical and structural features will be reconstructed in anatomical models of the heart. Electrophysiological properties will be used to parametrize mathematical models of the electrophysiology for the specific conditions investigated. The anatomically-based multiscale electrophysiological models will be used to conduct high performance computing simulations to investigate key factors determining the clinical phenotype and to evaluate the outcome of specific therapies. This research will be supported by the department's existing work on building multiscale models
for high performance simulations of the ECG.
The project will investigate cardiac phenotypes in cardiovascular disease using a combination of machine learning, signal processing and computer modelling. Experimental and clinical data such as the electrocardiogram and cardiac magnetic resonance will be analysis using signal and image processing techniques to quantify key properties for characterisation of the disease effects on the heart of patients affected by specifci disease conditions. Anatomical and structural features will be reconstructed in anatomical models of the heart. Electrophysiological properties will be used to parametrize mathematical models of the electrophysiology for the specific conditions investigated. The anatomically-based multiscale electrophysiological models will be used to conduct high performance computing simulations to investigate key factors determining the clinical phenotype and to evaluate the outcome of specific therapies. This research will be supported by the department's existing work on building multiscale models
for high performance simulations of the ECG.
Organisations
People |
ORCID iD |
| Adam McCarthy (Student) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/N509711/1 | 30/09/2016 | 29/09/2021 | |||
| 1746793 | Studentship | EP/N509711/1 | 02/10/2016 | 30/03/2020 | Adam McCarthy |