Document Detail


Resolution strategies for the finite-element-based solution of the ECG inverse problem.
MedLine Citation:
PMID:  19535314     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Successful employment of numerical techniques for the solution of forward and inverse ECG problems requires the ability to both quantify and minimize approximation errors introduced as part of the discretization process. Our objective is to develop discretization and refinement strategies involving hybrid-shaped finite elements so as to minimize approximation errors for the ECG inverse problem. We examine both the ill-posedness of the mathematical inverse problem and the ill-conditioning of the discretized system in order to propose strategies specifically designed for the ECG inverse problem. We demonstrate that previous discretization and approximation strategies may worsen the properties of the inverse problem approximation. We then demonstrate the efficacy of our strategies on both a simplified and a realistic 2-D torso model.
Authors:
Dafang Wang; Robert M Kirby; Chris R Johnson
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, U.S. Gov't, Non-P.H.S.     Date:  2009-06-16
Journal Detail:
Title:  IEEE transactions on bio-medical engineering     Volume:  57     ISSN:  1558-2531     ISO Abbreviation:  IEEE Trans Biomed Eng     Publication Date:  2010 Feb 
Date Detail:
Created Date:  2010-01-21     Completed Date:  2010-04-02     Revised Date:  2010-09-27    
Medline Journal Info:
Nlm Unique ID:  0012737     Medline TA:  IEEE Trans Biomed Eng     Country:  United States    
Other Details:
Languages:  eng     Pagination:  220-37     Citation Subset:  IM    
Affiliation:
Computing and Imaging (SCI) Institute and the School of Computing, University of Utah, Salt Lake City, UT 84112, USA. dfwang@sci.utah.edu
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Computer Simulation
Electrocardiography / methods*
Finite Element Analysis*
Humans
Image Processing, Computer-Assisted / methods*
Models, Biological*
Signal Processing, Computer-Assisted*
Grant Support
ID/Acronym/Agency:
5P41RR012553-10/RR/NCRR NIH HHS; P41 RR012553-10/RR/NCRR NIH HHS
Comments/Corrections

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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