Med Biol Eng Comput - Genetic algorithm-based regularization parameter estimation for the inverse electrocardiography problem using multiple constraints.

Tópicos

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Resumo

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.

Resumo Limpo

invers electrocardiographi goal estim cardiac electr sourc potenti measur bodi surfac natur illpos problem regular must employ obtain reliabl solut paper employ multipl constraint solut approach propos brook et al ieee tran biom eng extend practic applic includ two constraint find appropri valu multipl regular paramet propos use realvalu genet algorithm estim multipl regular paramet theoret possibl includ mani constraint necessari find correspond regular paramet use approach shown feasibl method use two three constraint result indic ga good approach estim multipl regular paramet

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