Large-scale parameter extraction in electrocardiology models through Born approximation

Handle URI:
http://hdl.handle.net/10754/562453
Title:
Large-scale parameter extraction in electrocardiology models through Born approximation
Authors:
He, Yuan; Keyes, David E. ( 0000-0002-4052-7224 )
Abstract:
One of the main objectives in electrocardiology is to extract physical properties of cardiac tissues from measured information on electrical activity of the heart. Mathematically, this is an inverse problem for reconstructing coefficients in electrocardiology models from partial knowledge of the solutions of the models. In this work, we consider such parameter extraction problems for two well-studied electrocardiology models: the bidomain model and the FitzHugh-Nagumo model. We propose a systematic reconstruction method based on the Born approximation of the original nonlinear inverse problem. We describe a two-step procedure that allows us to reconstruct not only perturbations of the unknowns, but also the backgrounds around which the linearization is performed. We show some numerical simulations under various conditions to demonstrate the performance of our method. We also introduce a parameterization strategy using eigenfunctions of the Laplacian operator to reduce the number of unknowns in the parameter extraction problem. © 2013 IOP Publishing Ltd.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Applied Mathematics and Computational Science Program; Extreme Computing Research Center
Publisher:
IOP Publishing
Journal:
Inverse Problems
Issue Date:
4-Dec-2012
DOI:
10.1088/0266-5611/29/1/015001
Type:
Article
ISSN:
02665611
Sponsors:
We would like to thank the anonymous referees for their constructive comments which improved the quality of this work. The work of YH is supported partially by an ICES Fellowship from the University of Texas at Austin.
Appears in Collections:
Articles; Applied Mathematics and Computational Science Program; Extreme Computing Research Center; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorHe, Yuanen
dc.contributor.authorKeyes, David E.en
dc.date.accessioned2015-08-03T10:38:44Zen
dc.date.available2015-08-03T10:38:44Zen
dc.date.issued2012-12-04en
dc.identifier.issn02665611en
dc.identifier.doi10.1088/0266-5611/29/1/015001en
dc.identifier.urihttp://hdl.handle.net/10754/562453en
dc.description.abstractOne of the main objectives in electrocardiology is to extract physical properties of cardiac tissues from measured information on electrical activity of the heart. Mathematically, this is an inverse problem for reconstructing coefficients in electrocardiology models from partial knowledge of the solutions of the models. In this work, we consider such parameter extraction problems for two well-studied electrocardiology models: the bidomain model and the FitzHugh-Nagumo model. We propose a systematic reconstruction method based on the Born approximation of the original nonlinear inverse problem. We describe a two-step procedure that allows us to reconstruct not only perturbations of the unknowns, but also the backgrounds around which the linearization is performed. We show some numerical simulations under various conditions to demonstrate the performance of our method. We also introduce a parameterization strategy using eigenfunctions of the Laplacian operator to reduce the number of unknowns in the parameter extraction problem. © 2013 IOP Publishing Ltd.en
dc.description.sponsorshipWe would like to thank the anonymous referees for their constructive comments which improved the quality of this work. The work of YH is supported partially by an ICES Fellowship from the University of Texas at Austin.en
dc.publisherIOP Publishingen
dc.titleLarge-scale parameter extraction in electrocardiology models through Born approximationen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.contributor.departmentExtreme Computing Research Centeren
dc.identifier.journalInverse Problemsen
dc.contributor.institutionInstitute for Computational Science and Engineering, University of Texas at Austin, Austin, TX 78712, United Statesen
dc.contributor.institutionDepartment of Applied Physics and Applied Mathematics, Columbia University, New York, NY 10027, United Statesen
kaust.authorKeyes, David E.en
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