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Parametric Electrical Modelling of Human Forearm Simulation Response Using Multi-frequency Electrical Bioimpedance

Anand, G; Lowe, A; Al-Jumaily, AM
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http://hdl.handle.net/10292/10710
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Abstract
This work presents a parametric electrical modelling of the electrical response of human forearm tissues through a simulation of Multi-frequency Electrical Bioimpedance analysis (MF-EBIA). The objective is to estimate an electrical equivalent representation for tissue response in terms of resistance and capacitance values for three tissue layers in the forearm – the fat, muscle and artery, using parametric fitting analysis. Following up from a simulation study of the human forearm model using Ansys® High Frequency Structure Simulator (HFSS), this work assumes an electrical model of the human forearm section for every tissue and calculates the electrical parameters. The tissue model was considered to be isotropic with regards to the dielectric properties and the consideration of blood flow was realised by taking three instances of radial artery diameter. The proposed model was validated by using the obtained values of model components to reproduce the overall response. The obtained values of resistance and capacitance for every tissue domain provide an insight into their significant contribution to the overall electrical response, which can be important while analysing their individual electrical behaviour and also helpful in various pre-experimental studies related to dielectric characterization of living tissues.
Keywords
Bioimpedance; Human forearm; Electrical modelling; Dielectric properties
Date
2016
Source
Journal of Biosensors & Bioelectronics, 7: 206. doi:10.4172/2155-6210.1000206
Item Type
Journal Article
Publisher
OMICS International
DOI
10.4172/2155-6210.1000206
Publisher's Version
https://www.omicsonline.org/open-access/parametric-electrical-modelling-of-human-forearm-simulation-responseusing-multifrequency-electrical-bioimpedance-2155-6210-1000206.php?aid=73495
Rights Statement
© 2016 Anand G, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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