Presentation Title

Analyzing Electrical Impedance Myography Parameters to Identify the Least Affected Parameter with Alteration in Subcutaneous Fat Thickness

Location

Room 2903

Session Format

Paper Presentation

Research Area Topic:

Engineering and Material Sciences - Electrical

Co-Presenters, Co- Authors, Co-Researchers, Mentors, or Faculty Advisors

Faculty Adviser - Dr. Mohammad Ahad, Assistant Professor, Department of Electrical Engineering.

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 analysed to isolate diseased muscles from healthy one. Beside muscle properties, some other factors like subcutaneous fat (SF) thickness, inter-electrode distance, muscle thickness etc. also impact the three above major EIM parameters. The purpose of this study is to explore the effect of SF thickness variation on different EIM parameters and propose a parameter which is least effected and also can detect muscle conditions. As Finite Element Method (FEM) has been established as an appropriate approach for analysis of non-symmetrical shape like muscle tissue for assessing alternations of muscle in disease-induced changes through ElM, in this study, we analysed the effect of SF thickness on EIM parameters using a finite element model of human upper arm which is developed based on anatomic data; material properties of the tissue obtained from rat and published sources. We analysed four different parameters in this study for various SF thicknesses. For example, resistance in normal condition and at 50 kHz varies 24.48% with per millimetre SF thickness variation while phase varies 4.01%. Reactance at 50 kHz varies 1.36% per millimetre of SF thickness for normal muscle. In conclusion, among the observed parameters, percentage change in reactance is the minimum with fat thickness variation while effectively identifying different muscle conditions. So we propose to use reactance as the principal parameter while analysing EIM in disease detection.

Keywords

EIM, EIM parameters, SF thickness, FEM

Presentation Type and Release Option

Presentation (Open Access)

Start Date

4-24-2015 1:30 PM

End Date

4-24-2015 2:30 PM

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Apr 24th, 1:30 PM Apr 24th, 2:30 PM

Analyzing Electrical Impedance Myography Parameters to Identify the Least Affected Parameter with Alteration in Subcutaneous Fat Thickness

Room 2903

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 analysed to isolate diseased muscles from healthy one. Beside muscle properties, some other factors like subcutaneous fat (SF) thickness, inter-electrode distance, muscle thickness etc. also impact the three above major EIM parameters. The purpose of this study is to explore the effect of SF thickness variation on different EIM parameters and propose a parameter which is least effected and also can detect muscle conditions. As Finite Element Method (FEM) has been established as an appropriate approach for analysis of non-symmetrical shape like muscle tissue for assessing alternations of muscle in disease-induced changes through ElM, in this study, we analysed the effect of SF thickness on EIM parameters using a finite element model of human upper arm which is developed based on anatomic data; material properties of the tissue obtained from rat and published sources. We analysed four different parameters in this study for various SF thicknesses. For example, resistance in normal condition and at 50 kHz varies 24.48% with per millimetre SF thickness variation while phase varies 4.01%. Reactance at 50 kHz varies 1.36% per millimetre of SF thickness for normal muscle. In conclusion, among the observed parameters, percentage change in reactance is the minimum with fat thickness variation while effectively identifying different muscle conditions. So we propose to use reactance as the principal parameter while analysing EIM in disease detection.