Optimizing Electrode Configuration for Electrical Impedance Measurements of Muscle via the Finite Element Method

Electrical impedance myography (EIM) is a technique for the evaluation of neuromuscular diseases, including amyotrophic lateral sclerosis and muscular dystrophy. In this study, we evaluated how alterations in the size and conductivity of muscle and thickness of subcutaneous fat impact the EIM data, with the aim of identifying an optimized electrode configuration for EIM measurements. Finite element models were developed for the human upper arm based on anatomic data; material properties of the tissues were obtained from rat and published sources. The developed model matched the frequency-dependent character of the data. Of the three major EIM parameters, resistance, reactance, and phase, the reactance was least susceptible to alterations in the subcutaneous fat thickness, regardless of electrode arrangement. For example, a quadrupling of fat thickness resulted in a 375% increase in resistance at 35 kHz but only a 29% reduction in reactance. By further optimizing the electrode configuration, the change in reactance could be reduced to just 0.25%. For a fixed 30 mm distance between the sense electrodes centered between the excitation electrodes, an 80 mm distance between the excitation electrodes was found to provide the best balance, with a less than 1% change in reactance despite a doubling of subcutaneous fat thickness or halving of muscle size. These analyses describe a basic approach for further electrode configuration optimization for EIM

Assessment of optimized electrode configuration in Electrical Impedance Myography study using genetic algorithm 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 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

Optimizing Electrode Configuration for Electrical Impedance Measurements of Muscle via the Finite Element Method

Electrical impedance myography (EIM) is a technique for the evaluation of neuromuscular diseases, including amyotrophic lateral sclerosis and muscular dystrophy. In this study, we evaluated how alterations in the size and conductivity of muscle and thickness of subcutaneous fat impact the EIM data, with the aim of identifying an optimized electrode configuration for EIM measurements. Finite element models were developed for the human upper arm based on anatomic data; material properties of the tissues were obtained from rat and published sources. The developed model matched the frequency-dependent character of the data. Of the three major EIM parameters, resistance, reactance, and phase, the reactance was least susceptible to alterations in the subcutaneous fat thickness, regardless of electrode arrangement. For example, a quadrupling of fat thickness resulted in a 375% increase in resistance at 35 kHz but only a 29% reduction in reactance. By further optimizing the electrode configuration, the change in reactance could be reduced to just 0.25%. For a fixed 30 mm distance between the sense electrodes centered between the excitation electrodes, an 80 mm distance between the excitation electrodes was found to provide the best balance, with a less than 1% change in reactance despite a doubling of subcutaneous fat thickness or halving of muscle size. These analyses describe a basic approach for further electrode configuration optimization for EIM