Genetic algorithm-based regularization parameter estimation for the inverse electrocardiography problem using multiple constraints

Date

2013-04-01

Author

Serinağaoğlu Doğrusöz, Yeşim
Gavgani, Alireza Mazloumi

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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.

Subject Keywords

Inverse electrocardiography, Multiple constraints, Regularization parameter, Genetic algorithm

URI

https://hdl.handle.net/11511/33331

Journal

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING

DOI

https://doi.org/10.1007/s11517-012-1005-6

Collections

Department of Electrical and Electronics Engineering, Article

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Citation Formats

Y. Serinağaoğlu Doğrusöz and A. M. Gavgani, “Genetic algorithm-based regularization parameter estimation for the inverse electrocardiography problem using multiple constraints,” MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, pp. 367–375, 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/33331.