Performance of Tikhonov Regularization with Different Constraints in Electrocardiographic Imaging

The purpose of electrocardiographic imaging (ECGI) is to obtain high-resolution information about the electrical activity of the heart. ECGI is the estimation of heart’s electrical activity using body surface potentials and a mathematical model that includes the torso, heart geometry, and tissue conductivity information between these surfaces. Tikhonov regularization is one of the most preferred methods in ECGI due to its fast and straightforward applicability. In this study, we employed zero-order, second-order, and fractional Tikhonov solution techniques for the ECGI problem and assessed their accuracy and robustness against the measurement noise.