Abstract
In inverse electrocardiography, the goal is to estimate cardiac electrical sources from potential measurements on the body surface. It is by nature an ill-posed problem, and regularization must be employed to obtain reliable solutions. This paper employs the multiple constraint solution approach proposed in Brooks et al. (IEEE Trans Biomed Eng 46(1):3–18, 1999) and extends its practical applicability to include more than two constraints by finding appropriate values for the multiple regularization parameters. Here, we propose the use of real-valued genetic algorithms for the estimation of multiple regularization parameters. Theoretically, it is possible to include as many constraints as necessary and find the corresponding regularization parameters using this approach. We have shown the feasibility of our method using two and three constraints. The results indicate that GA could be a good approach for the estimation of multiple regularization parameters.
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Acknowledgments
This work was supported by The Scientific and Technological Research Council of Turkey, grant number 105E070.The authors would like to thank Dr. R. S. Macleod from the University of Utah, Nora Eccles Harrison Cardiovascular Research and Training Institute, for the data used in this study. This work was made possible in part by software (Map3d) from the NIH/NCRR Center for Integrative Biomedical Computing, P41-RR12553-10.
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Serinagaoglu Dogrusoz, Y., Mazloumi Gavgani, A. Genetic algorithm-based regularization parameter estimation for the inverse electrocardiography problem using multiple constraints. Med Biol Eng Comput 51, 367–375 (2013). https://doi.org/10.1007/s11517-012-1005-6
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DOI: https://doi.org/10.1007/s11517-012-1005-6