[Thesis]. Manchester, UK: The University of Manchester; 2016.
Cardiac disease is one of the leading causes of death in the world, with an increase
in cardiac arrhythmias in recent years. In addition, myocardial ischemia, which arises
from the lack of blood in the cardiac tissue, can lead to cardiac arrhythmias and
even sudden cardiac death. Cardiac arrhythmias, such as atrial fibrillation, are characterised
by abnormal wave excitation and repolarization patterns in the myocardial tissue.
These abnormal patterns are usually diagnosed through non-invasive electrical measurements
on the surface of the body, i.e., the electrocardiogram (ECG). However, the most common
lead configuration of the ECG, the 12-lead ECG, has its limitations in providing sufficient
information to identify and locate the origin of cardiac arrhythmias. Therefore, there
is an increasing need to develop novel methods to diagnose and find the origin of
arrhythmic excitation, which will increase the efficacy of the treatment and diagnosis
of cardiac arrhythmias.The objective of this research was to develop a family of multi-scale
computational models of the human heart and thorax to simulate and investigate the
effect of arrhythmic electrical activity in the heart on the electric and magnetic
activities on the surface of the body. Based on these simulations, new theoretical
algorithms were developed to non-invasively diagnose the origins of cardiac arrhythmias,
such as the location of ectopic activities in the atria or ischemic regions within
the ventricles, which are challenging to the clinician. These non-invasive diagnose
methods were based on the implementation of multi-lead ECG systems, magnetocardiograms
(MCGs) and electrocardiographic imaging.