Simulation study of the effect of modelling errors on the solution of inverse cardiac source imaging problem using realistic source patterns

The effect of the modelling errors on the solution of the inverse problem of electrocardiography is investigated. The electrocardiographic signal is simulated using a finite element model of the human torso and realistic source patterns gained with a cellular automaton. Noise is added to simulated measurements and the inverse problem is solved. The modelling errors consist of false conductivity assumptions, changed anisotropy ratio of skeletal muscles and geometric errors. The effect of modelling errors on optimal regularization parameter determination is investigated. The changes in muscle anisotropy and heart position are shown to have the highest effect on reconstructed epicardial potentials. CRESO (Composite REsidual and Smoothing Operator) and L-curve criteria for optimal regularization parameter estimation are compared