Estimation of electrical conductivity of a layered spherical head model using electrical impedance tomography

Electrical Impedance Tomography (EIT) is a non-invasive method that aims to create an electrical conductivity map of a volume. In particular, it can be applied to study the human head. The method consists on the injection of an unperceptive and known current through two electrodes attached to the sc...

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Detalles Bibliográficos
Autores principales: Fernández Corazza, Mariano, Ellenrieder, Nicolás von, Muravchik, Carlos Horacio
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2011
Materias:
Física
Tomografía
Conductividad Eléctrica
Impedancia Eléctrica
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/84314
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-84314
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Física
Tomografía
Conductividad Eléctrica
Impedancia Eléctrica
spellingShingle Física
Tomografía
Conductividad Eléctrica
Impedancia Eléctrica
Fernández Corazza, Mariano
Ellenrieder, Nicolás von
Muravchik, Carlos Horacio
Estimation of electrical conductivity of a layered spherical head model using electrical impedance tomography
topic_facet Física
Tomografía
Conductividad Eléctrica
Impedancia Eléctrica
description Electrical Impedance Tomography (EIT) is a non-invasive method that aims to create an electrical conductivity map of a volume. In particular, it can be applied to study the human head. The method consists on the injection of an unperceptive and known current through two electrodes attached to the scalp, and the measurement of the resulting electric potential distribution at an array of sensors also placed on the scalp. In this work, we propose a parametric estimation of the brain, scalp and skull conductivities using EIT over an spherical model of the head. The forward problem involves the computation of the electric potential on the surface, for given the conductivities and the injection electrode positions, while the inverse problem consists on estimating the conductivities given the sensor measurements. In this study, the analytical solution to the forward problem based on a three layer spherical model is first described. Then, some measurements are simulated adding white noise to the solutions and the inverse problem is solved in order to estimate the brain, skull and scalp conductivity relations. This is done with a least squares approach and the Nelder-Mead multidimensional unconstrained nonlinear minimization method.
format Objeto de conferencia
Objeto de conferencia
author Fernández Corazza, Mariano
Ellenrieder, Nicolás von
Muravchik, Carlos Horacio
author_facet Fernández Corazza, Mariano
Ellenrieder, Nicolás von
Muravchik, Carlos Horacio
author_sort Fernández Corazza, Mariano
title Estimation of electrical conductivity of a layered spherical head model using electrical impedance tomography
title_short Estimation of electrical conductivity of a layered spherical head model using electrical impedance tomography
title_full Estimation of electrical conductivity of a layered spherical head model using electrical impedance tomography
title_fullStr Estimation of electrical conductivity of a layered spherical head model using electrical impedance tomography
title_full_unstemmed Estimation of electrical conductivity of a layered spherical head model using electrical impedance tomography
title_sort estimation of electrical conductivity of a layered spherical head model using electrical impedance tomography
publishDate 2011
url http://sedici.unlp.edu.ar/handle/10915/84314
work_keys_str_mv AT fernandezcorazzamariano estimationofelectricalconductivityofalayeredsphericalheadmodelusingelectricalimpedancetomography
AT ellenriedernicolasvon estimationofelectricalconductivityofalayeredsphericalheadmodelusingelectricalimpedancetomography
AT muravchikcarloshoracio estimationofelectricalconductivityofalayeredsphericalheadmodelusingelectricalimpedancetomography
bdutipo_str Repositorios
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