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Combination of conventional regularization methods and genetic algorithm for solving the inverse problem of electrocardiography

Distribution of electrical potentials over the surface of the heart, which is called the epicardial potential distribution, is a valuable tool to understand whether there is a defect in the heart. Direct measurement of these potentials requires highly invasive procedures. An alternative is to reconstruct these epicardial potentials non-invasively from the body surface potentials, which constitutes one form of the ill-posed inverse problem of electrocardiography (ECG). The goal of this study is to solve the inverse problem of ECG using several regularization methods and compare their performances. We employed Tikhonov Regularization, Truncated Singular Value Decomposition (TSVD), Least Squares QR (LSQR) methods in this study. We compared the effectiveness of these regularization methods to solve the ill-posed inverse ECG problem. Some of the regularization methods require a regularization parameter to solve the inverse problem. We used the well-known L-Curve method to obtain the regularization parameter. The performance of the regularization methods for solving the inverse ECG problem was also evaluated based on a realistic heart-torso model simulation protocol. In this paper, we also investigated the usage of genetic algorithm (GA) for regularizing the ill-posed inverse ECG problem. The results showed that GA can be applied to regularize the ill-posed problem when combined with the results of conventional regularization methods or additional information about solutions.