Term of Award

Spring 2016

Degree Name

Master of Science in Applied Engineering (M.S.A.E.)

Document Type and Release Option

Thesis (open access)

Department

Department of Electrical Engineering

Committee Chair

Mohammad Ahad

Committee Member 1

Danda Rawat

Committee Member 2

Rocio Alba-Flores

Abstract

Electrical Impedance Myography (EIM) is a neurophysiologic technique in which high- frequency, low-intensity electrical current is applied via surface electrodes over a muscle or muscle group of interest and the resulting electrical parameters (resistance, reactance and phase) are analyzed to isolate diseased muscles from healthy ones. Beside muscle properties, some other anatomic and non-anatomic factors like muscle shape, subcutaneous fat (SF) thickness, inter-electrode distance, etc. also impact the major EIM parameters and thus affect the EIM analysis outcomes. The purpose of this study is to explore the effects of variation in some of these factors impose on EIM parameters and propose an optimum electrode configuration which is least affected by these anatomic and non-anatomic factors without compromising EIM’s ability to detect muscle conditions. In this study, genetic algorithm was applied as an optimization tool in order to find out an optimized electrode setup, which is less prone to these factors other than muscle properties. The results obtained suggest 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.

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