Assessment of Electrode Configurations of Electrical Impedance Myography for the Evaluation of Neuromuscular Diseases

<p>Electrical impedance myography (EIM) is a painless, noninvasive approach to measure the neuromuscular disease status. EIM parameters- resistance (R), reactance (X) and phase (θ) depend significantly on subcutaneous fat thickness, muscle size and inter electrode distance. The objective of this research is to find an electrode configuration which can minimize the effects on EIM parameters due to subcutaneous fat thickness variation. In this study, a model of human upper arm was developed using finite element method (FEM), which has already been established as an appropriate approach for the analysis of non-symmetrical shape for assessing alternations of muscle in disease-induced changes through EIM. Finite element model with two different kinds of electrode shapes namely rectangular (conventional shape) and circular shapes (proposed shape) were designed for a subcutaneous fat range of 5mm to 25mm. The results show that the standard deviation of reactance values measured for this specified range of fat thickness is 0.65 Ω for circular electrodes, whereas for the rectangular electrode this value is 2.04 Ω. Finally, genetic algorithm was implemented to find an optimized electrode shape which also indicates that the conventional rectangular electrode shape is not the ideal shape for EIM measurements.</p>

Name thesis:degree_name

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

Level thesis:degree_level

Thesis (open access)

Discipline thesis:degree_discipline

Department of Mechanical Engineering

Year dc:date.available

2015

Author dc:creator

• Fazle Rabbi, Khondokar Mohammad

Contributors dc:contributor

• Rocio Alba-Flores
• Danda B Rawat

• ETD
• EIM
• EMG
• Neuromuscular
• Muscle
• Impedance
• Genetic algorithm
• Biomedical
• Biomedical Devices and Instrumentation
• Electrical and Electronics

Repository record dc:identifier

https://digitalcommons.georgiasouthern.edu/etd/1273

OAI identifier oai:identifier

oai:digitalcommons.georgiasouthern.edu:etd-2325