"Due to population aging , the incidence of atrial fibrillation (AF) increases tremendously. Although not lethal, AF reduces exercise capacity and strongly increases the risk of stroke. Despite significant progress in understanding the pathophysiology of AF, its treatment is still far from satisfactory. AF is a progressive disease. With time, paroxysmal AF becomes persistent, and the success rate of therapies declines. The progression of AF is related to a slow but steady process of remodeling in the atria, characterized by hypertrophy, replacement of muscle cells by connective tissue, and changes in gap junctions. The complexity of the propagating activation wavefront in the atria is a good measure of the disease stage. Current methods try to infer this complexity from frequency analysis. A truly spatio-temporal quantification of wavefront complexity requires invasive electrophysiological investigation. For broad-scale clinical practice, a noninvasive assessment from surface electrocardiograms (ECG) is needed. Our group is running an ambitious clinical study in which high-resolution ECGs are compared with invasive electrical data in the same patient. We propose to augment this study with computer modeling to provide insights in the relationship between disease states and ECG parameters. State-of-the-art methods will be used to compute propagating activation in 3-D models of the atria in different stages of disease. Using a realistic torso model, surface ECGs will be computed from the propagating activation, and the relation between wavefront complexity in the atria and spatial complexity of the ECG will be investigated. The model will be validated with our clinical and experimental data. As a result of this work, we expect to optimize diagnostic methods and choice of treatment for AF, and thus to help reduce the increasing socio-economic burden of this arrhythmia."