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Estimation of Multi-Layer Tissue Conductivities from Non-invasively Measured Bioresistances Using Divided Electrodes

  • ZHAO Xueli
    the Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Tokushima
  • KINOUCHI Yohsuke
    the Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Tokushima
  • IRITANI Tadamitsu
    the Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Tokushima
  • MORIMOTO Tadaoki
    the School of Medical Science, University of Tokushima
  • TAKEUCHI Mieko
    the School of Medical Science, University of Tokushima

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Abstract

To estimate inner multi-layer tissue conductivity distribution in a cross section of the local tissue by using bioresistance data measured noninvasively on the surface of the tissue, a measurement method using divided electrodes is proposed, where a current electrode is divided into several parts. The method is evaluated by computer simulations using a three-dimension (3D) model and two two-dimension (2D) models. In this paper, conductivity distributions of the simplified (2D) model are analyzed based on a combination of a finite difference method (FDM) and a steepest descent method (SDM). Simulation results show that conductivity values for skin, fat and muscle layers can be estimated with an error less than 0.1%. Even though different strength random noise is added to measured resistance values, the conductivities are estimated with reasonable precise, e.g., the average error is about 4.25% for 10% noise. The configuration of the divided electrodes are examined in terms of dividing pattern and the size of surrounding guard electrodes to confine and control the input currents from the divided electrodes within a cross sectional area in the tissue.

Journal

  • IEICE Trans Inf Syst., D

    IEICE Trans Inf Syst., D 85 (6), 1031-1038, 2002-06-01

    The Institute of Electronics, Information and Communication Engineers

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Details

  • CRID
    1573668927255636608
  • NII Article ID
    110003210652
  • NII Book ID
    AA10826272
  • ISSN
    09168532
  • Text Lang
    en
  • Data Source
    • CiNii Articles

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