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Investigation of physiological swelling on conductivity distribution in lower leg subcutaneous tissue by electrical impedance tomography

Journal of Electrical Bioimpedance
Volume 11 (2020): Issue 1 (January 2020)
By:
Open Access
|May 2020

Figures & Tables

Fig. 1

The FEM forward mesh based on an MRI image. White, red, and blue part indicate SAT layer, muscle, and bone, respectively.
The FEM forward mesh based on an MRI image. White, red, and blue part indicate SAT layer, muscle, and bone, respectively.

Fig. 2

Experimental setup.
Experimental setup.

Fig. 3

Schematic diagram of the experimental schedule. After the 3rd measurement (t = 75 min), the subject did exercise to promote varying water contents compared to the original physiological state at the beginning of the experiment.
Schematic diagram of the experimental schedule. After the 3rd measurement (t = 75 min), the subject did exercise to promote varying water contents compared to the original physiological state at the beginning of the experiment.

Fig. 4

(a) the normalized absolute conductivity distribution of the right lower leg (viewed from the top) reconstructed by absolute EIT at 1st measurement; These are used as inhomogeneous reference data in (b). Time series of reconstructed relative conductivity distribution using the image-based reference EIT (b) and conventional time difference EIT (c) at 2nd, 3rd, and 4th measurement, respectively.
(a) the normalized absolute conductivity distribution of the right lower leg (viewed from the top) reconstructed by absolute EIT at 1st measurement; These are used as inhomogeneous reference data in (b). Time series of reconstructed relative conductivity distribution using the image-based reference EIT (b) and conventional time difference EIT (c) at 2nd, 3rd, and 4th measurement, respectively.

Fig. 5

The normalized temporal variation over segmental conductivity σseg in the predicted subcutaneous layer (white part in Fig.1) and segmental extracellular water volume Vseg in the right leg from image-based reference EIT (IBR-EIT) and MFBIA data, respectively.
The normalized temporal variation over segmental conductivity σseg in the predicted subcutaneous layer (white part in Fig.1) and segmental extracellular water volume Vseg in the right leg from image-based reference EIT (IBR-EIT) and MFBIA data, respectively.

Fig. 6

The magnitude of impedance at 0 min and 60 min on Day 1.
The magnitude of impedance at 0 min and 60 min on Day 1.

Schematic flow of image-based reference EIT_

Step 1:
Defining the filtering method to eliminate the muscle’s conductivity distribution and unexpected noise background
Obtain: Equation (4)
Step 2:
Using voltage data t = 0 as an initial conductivity distribution, selecting the mesh of SAT on the forward problem mesh condition, calculating the fat weighted threshold value σ^fat¯+std(σ^fat) \overline {{{\hat \sigma}_{fat}}} + std\left({{{\hat \sigma}_{fat}}} \right)
Obtain: the fat weighted threshold value σ^fat¯+std(σ^fat)s \overline {{{\hat \sigma}_{fat}}} + std{\left({{{\hat \sigma}_{fat}}} \right)_s}
Step 3:
Applying the filter on equation (4) to experimental data
Obtain:σabs and σ
Step 4:
Using voltage data t = 30, 60, and 90 mins to evaluate the varying water content to obtain the conductivity distribution, applying equation (5)
Obtain: σdiff
DOI: https://doi.org/10.2478/joeb-2020-0004 | Journal eISSN: 1891-5469
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Language: English
Page range: 19 - 25
Submitted on: Feb 4, 2020
Published on: May 14, 2020
Published by: University of Oslo
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
Subcutaneous adipose tissue assessment,
Electrical Impedance Tomography,
Wearable sensor,
Physiological swelling,
Multi-Frequency Bioelectrical Impedance Analysis
Related subjects:
Engineering,
Bioengineering and biomedical engineering,
Biomedical electronics,
Life sciences,
Biophysics,
Medicine,
Biomedical engineering,
Physics,
Spectroscopy and metrology

© 2020 R. Ogawa, M. R. Baidillah, S. Akita, M. Takei, published by University of Oslo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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