The Influence of Stochastic Organ Conductivity in 2D ECG Forward Modeling: A Stochastic Finite Element Study

Author

Geneser, Sarah E. ; Choe, Seungkeol ; Kirby, Robert M. ; MacLeod, Robert S.

Author_Institution

Sch. of Comput. & Sci. Comput., Utah Univ., Salt Lake City, UT

fYear

2006

Firstpage

5528

Lastpage

5531

Abstract

Quantification of the sensitivity of the electro-cardiographic forward problem to various parameters can effectively direct the generalization of patient specific models without significant loss in accuracy. To this purpose we applied polynomial chaos based stochastic finite elements to assess the effect of variations in the distributions of tissue conductivity in a two-dimensional torso geometry generated from MRI scans and epicardial boundary conditions specified by intra-operatively recorded heart potentials. The polynomial chaos methodology allows sensitivity analysis of this type to be done in a fraction of the time required for a Monte Carlo analysis

Keywords

bioelectric potentials; biological organs; biological tissues; biomedical MRI; chaos; electrocardiography; finite element analysis; stochastic processes; MRI scans; electrocardiographic forward problem; epicardial boundary conditions; heart potentials; patient specific models; polynomial chaos; polynomial chaos methodology; sensitivity analysis; stochastic finite elements; stochastic organ conductivity; tissue conductivity; two-dimensional ECG forward modeling; two-dimensional torso geometry; Boundary conditions; Chaos; Conductivity; Electrocardiography; Finite element methods; Geometry; Magnetic resonance imaging; Polynomials; Stochastic processes; Torso; forward problems; polynomial chaos; stochastic processes; uncertainty quantification;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the

Conference_Location

Shanghai

Print_ISBN

0-7803-8741-4

Type

conf

DOI

10.1109/IEMBS.2005.1615736

Filename

1615736