Use of genetic algorithm for selection of regularization parameters in multiple constraint inverse ECG problem

Tikhonov regularization is one of the most widely used regularization approaches in literature to overcome the ill-posedness of the inverse electrocardiography problem. However, the resulting solutions are biased towards the constraint used for regularization. One alternative to obtain improved results is to employ multiple constraints in the cost function. This approach has been shown to produce better results; however finding appropriate regularization parameters is a serious limitation of the method. In this study, we propose estimating multiple regularization parameters using a genetic algorithm based approach. Applicability of the approach is demonstrated here using two and three constraints. The results show that GA based multiple constraints approach improves the Tikhonov regularization solutions.