On the influence of spread constant in radial basis networks for electrical impedance tomography

Sébastien Martin, T.m. Choi

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Electrical impedance tomography (EIT) is a non-invasive imaging technique. The main task of this work is to solve a non-linear inverse problem, for which several techniques have been suggested, but none of which gives a very high degree of accuracy. This paper introduces a novel approach, based on radial basis function (RBF) artificial neural networks (ANNs), to solve this problem, and uses several ANNs to obtain the best solution to the EIT inverse problem. ANNs have the potential to directly estimate the solution of the inverse problem with a high degree of accuracy. While different radial basis neural networks do not always perform well on different problems, they usually give good results on some specific problems. This paper evidences a strong correlation between the area of the target and the spread constant of the RBF network that gives the best reconstruction. A solution to automatically estimate the size of the target and pick the best neural network directly from voltage measurements is presented, making the reconstruction process automatic. By automatically selecting the best ANN for each specific set of voltage measurements, the proposed solution gives a more accurate reconstruction of both small and large targets.

Original languageEnglish
Pages (from-to)801-819
Number of pages19
JournalPhysiological Measurement
Volume37
Issue number6
DOIs
StatePublished - 20 May 2016

Keywords

  • artificial neural network
  • electrical impedance tomography
  • inverse problem
  • nonlinear optimization
  • radial basis function

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