Table of Contents
Journal of Medical Engineering
Volume 2016 (2016), Article ID 9123464, 7 pages
http://dx.doi.org/10.1155/2016/9123464
Research Article

Assessment of Optimized Electrode Configuration for Electrical Impedance Myography Using Genetic Algorithm via Finite Element Model

Department of Electrical Engineering, Georgia Southern University, Statesboro, GA 30458, USA

Received 30 June 2016; Revised 7 September 2016; Accepted 29 September 2016

Academic Editor: Panagiotis Kosmas

Copyright © 2016 Somen Baidya and Mohammad A. Ahad. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Electrical Impedance Myography (EIM) is a noninvasive neurophysiologic technique to diagnose muscle health. Besides muscle properties, the EIM measurements vary significantly with the change of some other anatomic and nonanatomic factors such as skin fat thickness, shape and thickness of muscle, and electrode size and spacing due to its noninvasive nature of measurement. In this study, genetic algorithm was applied along with finite element model of EIM as an optimization tool in order to figure out an optimized EIM electrode setup, which is less affected by these factors, specifically muscle thickness variation, but does not compromise EIM’s ability to detect muscle diseases. The results obtained suggest that a particular arrangement of electrodes and minimization of electrode surface area to its practical limit can overcome the effect of undesired factors on EIM parameters to a larger extent.