  Many types of ventricular and atrial cardiac arrhythmias have been discovered
in clinical practice in the past 100 years, and these arrhythmias are a major
contributor to sudden cardiac death. Ventricular tachycardia, ventricular
fibrillation, and paroxysmal atrial fibrillation are the most
commonly-occurring and dangerous arrhythmias, therefore early detection is
crucial to prevent any further complications and reduce fatalities. Implantable
devices such as pacemakers are commonly used in patients at high risk of sudden
cardiac death. While great advances have been made in medical technology, there
remain significant challenges in effective management of common arrhythmias.
This thesis proposes novel arrhythmia detection and prediction methods to
differentiate cardiac arrhythmias from non-life-threatening cardiac events, to
increase the likelihood of detecting events that may lead to mortality, as well
as reduce the incidence of unnecessary therapeutic intervention. The methods
are based on detailed analysis of Heart Rate Variability (HRV) information. The
results of the work show good performance of the proposed methods and support
the potential for their deployment in resource-constrained devices for
ventricular and atrial arrhythmia prediction, such as implantable pacemakers
and defibrillators.
