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Improving the spatial solution of electrocardiographic imaging: a new regularization parameter choice technique for the tikhonov method

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dc.contributor.author Chamorro Servent, Judit
dc.contributor.author Dubois, Rémi
dc.contributor.author Potse, Mark
dc.contributor.author Coudière, Yves
dc.date.accessioned 2018-06-07T15:34:47Z
dc.date.available 2018-06-07T15:34:47Z
dc.date.issued 2017
dc.identifier.citation Chamorro-Servent J, Dubois R, Potse M, Coudière Y. Improving the spatial solution of electrocardiographic imaging: a new regularization parameter choice technique for the tikhonov method. In: Pop M, Wright GA, editors. Functional Imaging and Modelling of the Heart 9th International Conference Proceedings, FIMH 2017; 2017 June 11-13; Toronto, Canada. [Cham]: Springer; 2017. p. 289-300.(LNCS; no. 10263). DOI: 10.1007/978-3-319-59448-4_28
dc.identifier.isbn 978-3-319-59448-4
dc.identifier.isbn 978-3-319-59447-7
dc.identifier.issn 0302-9743
dc.identifier.uri http://hdl.handle.net/10230/34857
dc.description Comunicació presentada a: The 9th International Conference Proceedings, FIMH 2017, celebrat de l'11 al 13 de juny de 2017 a Toronto, Canadà
dc.description.abstract The electrocardiographic imaging (ECGI) inverse problem is highly ill-posed and regularization is needed to stabilize the problem and to provide a unique solution. When Tikhonov regularization is used, choosing the regularization parameter is a challenging problem. Mathematically, a suitable value for this parameter needs to fulfill the Discrete Picard Condition (DPC). In this study, we propose two new methods to choose the regularization parameter for ECGI with the Tikhonov method: i) a new automatic technique based on the DPC, which we named ADPC, and ii) the U-curve method, introduced in other fields for cases where the well-known L-curve method fails or provides an over-regularized solution, and not tested yet in ECGI. We calculated the Tikhonov solution with the ADPC and U-curve parameters for in-silico data, and we compared them with the solution obtained with other automatic regularization choice methods widely used for the ECGI problem (CRESO and L-curve). ADPC provided a better correlation coefficient of the potentials in time and of the activation time (AT) maps, while less error was present in most of the cases compared to the other methods. Furthermore, we found that for in-silico spiral wave data, the L-curve method over-regularized the solution and the AT maps could not be solved for some of these cases. U-curve and ADPC provided the best solutions in these last cases.
dc.description.sponsorship This study received financial support from the French Government under the “Investments of the Future” program managed by the National Research 12 Agency (ANR), Grant reference ANR-10-IAHU-04 and from the Conseil Régional Aquitaine as part of the project “Assimilation de données en cancérologie et cardiologie”. This work was granted access to the HPC resources of TGCC under the allocation x2016037379 made by GENCI.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Springer
dc.relation.ispartof Pop M, Wright GA, editors. Functional Imaging and Modelling of the Heart 9th International Conference Proceedings, FIMH 2017; 2017 June 11-13; Toronto, Canada. [Cham]: Springer; 2017. p. 289-300.(LNCS; no. 10263).
dc.rights © Springer The final publication is available at Springer via https://doi.org/10.1007/978-3-319-59448-4_28
dc.title Improving the spatial solution of electrocardiographic imaging: a new regularization parameter choice technique for the tikhonov method
dc.type info:eu-repo/semantics/conferenceObject
dc.identifier.doi https://doi.org/10.1007/978-3-319-59448-4_28
dc.subject.keyword Inverse problem
dc.subject.keyword Regularization
dc.subject.keyword Electrocardiographic imaging
dc.subject.keyword Potentials
dc.subject.keyword Tikhonov regularization
dc.subject.keyword Ill-posed problems
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/acceptedVersion


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