Term of Award
Master of Science in Applied Engineering (M.S.A.E.)
Document Type and Release Option
Thesis (open access)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department of Electrical Engineering
Committee Member 1
Committee Member 2
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.
Baidya, Somen, "Assessment of optimized electrode configuration in Electrical Impedance Myography study using genetic algorithm via Finite Element Model" (2016). Electronic Theses and Dissertations. 1390.