Ventricular arrhythmias, such as ventricular ectopic beats (VEB) and ventricular tachycardias (VT), can increase the risk of syncopes or sudden cardiac death. VEB and VT are treated by cardiologists with catheter ablation. During this procedure, the physician finds the origin of the ectopic beat and removes the arrhythmogenic tissue. The process of finding the origin of the VEB is often very tedious and can lead to medical complications and further costs. In order to minimize risk and increase accuracy, methods for the direct localization of ectopic foci using only the body surface potential map (BSPM) are under development.
ECG imaging (ECGI) of the electrical activity of the heart is a completely non-invasive method. ECGI has the main goal of e.g. reconstructing the transmembrane voltages in the heart for a given BSPM. However, a full reconstruction of the transmembrane voltages is not necessary for finding the positions of the ectopic beats. In this project, modeling, advanced signal processing and machine learning methods will be applied to directly reconstruct the positions of the ectopic foci.