Improved cardiac arrhythmia prediction based on heart rate variability analysis

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. A range of features based on HRV analysis are investigated, including features from time, frequency, bispectrum and nonlinear analysis, and a range of classification techniques is used for prediction based on these features. Firstly, a thorough review of existing methods for ventricular arrhythmia prediction is conducted, including a detailed comparative experimental study. Following this, feature extraction for ventricular arrhythmia prediction is investigated using feature ranking methods based on mutual information. Using classification techniques such as support vector machine, k-nearest neighbour and random forests, the proposed approaches compare well with related work in the literature using different signal analysis durations. Furthermore, for paroxysmal atrial fibrillation prediction, seven novel features are proposed and investigated using a number of standard classification techniques. Using only the seven newly-proposed features, classification performance outperforms those of the classical state-of-the-art feature set, and the results further improve when the features are combined with several of the classical features. 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