Identifying least affected parameters in analyzing Electrical Impedance Myography with alteration in subcutaneous fat thickness via finite element model

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 one. Beside muscle properties, some other factors like subcutaneous fat (SF) thickness, inter-electrode distance, muscle thickness etc. also impact the 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 affected and also can detect muscle conditions. We analyzed four different parameters in this study for various SF thicknesses and none of them possesses constant profile with alteration in SF thickness. For example, resistance in normal condition varies 24.48% with per millimeter SF thickness variation while phase varies 4.01%. Further investigation shows that among the observed parameters percentage changes in reactance is minimum with fat thickness variation while effectively identifying different muscle conditions.