Cardiovascular disease is the most common cause of morbidity and mortality in developed countries. It is well known that myocardial ischemia predisposes ventricular tachyarrhythmias and fibrillation. Early and effective diagnosis of myocardial ischemia is essential for treating patients with cardiac ischemia to save their lives. The newly developed electrocardiographic imaging (ECGI) modality provides a promising technology for high-resolution mapping of the heart's electrical and magnetic dynamics in normal and pathological conditions. This approach uses an array of networked electrodes to directly reconstruct the heart's electrophysiological activity by using the full-body surface potential and solving the inverse problem in electrocardiography. This thesis's objective was to develop computational models to work in conjunction with biophysically accurate cardiac electrophysiological models to investigate and improve the detection of acute ventricular ischemia with the ECGI modality. Firstly, it focuses on improving model realism exploring the effect of using a limited number of measurement electrodes on the body's surface and then assessing how this affects the performance of common regularisation techniques. Secondly, it investigates how optimising electrode placement with genetic algorithms can lead to vastly improved ECGI results, facilitating the use of fewer electrodes than existing uniform layouts. Finally, the consequences of using ECG signal processing techniques are explored on the ECGI modality utilising a more sophisticated noise model that simulates electrocardiogram baseline wander noise and an array of signal processing methods.
|Date of Award||1 Aug 2021|
- The University of Manchester
|Supervisor||Jian Lu (Supervisor) & Henggui Zhang (Supervisor)|
- 3D heart