A Finite Element Analysis and Circuit Modelling Methodology for Studying Electrical Impedance Myography of Human Limbs

Abstract

Objective: Electrical impedance myography (EIM) measures bioimpedance over muscles. This paper proposes a circuit-based modelling methodology originated from finite element analysis (FEA), to emulate tissues and effects from anthropometric variations, and electrode placements, on EIM measurements. The proposed methodology is demonstrated on the upper arms and lower legs. Methods: FEA evaluates impedance spectra (Z-parameters), sensitivity, and volume impedance density for variations of subcutaneous fat thickness (t f ), muscle thickness (tm), and inter-electrode distance (IED), on limb models over 1Hz-1MHz frequency range. The limbs' models are based on simplified anatomical data and dielectric properties from published sources. Contributions of tissues to the total impedance are computed from impedance sensitivity and density. FEA Z-parameters are imported into a circuit design environment, and used to develop a three Cole dispersion circuit-based model. FEA and circuit model simulation results are compared with measurements on ten human subjects. Results: Muscle contributions are maximized at 31.25kHz and 62.5kHz for the upper arm and lower leg, respectively, at 4cm IED. The circuit model emulates variations in t f and tm, and simulates up to 89 times faster than FEA.The circuit model matches subjects measurements with RMS errors < 36.43Ω and < 17.28 • , while FEA does with < 36.59Ω and < 4.36 • . Conclusions: We demonstrate that FEA is able to estimate the optimal frequencies and electrode placements, and circuit-based modelling can accurately emulate the limbs' bioimpedance. Significance: The proposed methodology facilitates studying the impact of biophysical principles on EIM, enabling the development of future EIM acquisition systems.

Electrical impedance myography combined with quantitative assessment techniques in paretic muscle of stroke survivors: Insights and challenges

“…Standards for EIM parameters in clinical applications are still lacking, especially when the comparison of data across individuals is considered. As this technique is sensitive to muscle morphological, electrical properties and the electrode configuration ( Schrunder et al, 2022 ), it is then necessary to identify the impact of these factors and to explore approaches to minimize their effects. Computational modeling that incorporates detailed finite element models has been proven capable in studying the biophysical mechanisms of EIM and optimize the design of electrode arrangements ( Ahad and Rutkove, 2009 ; Jafarpoor et al, 2011 , 2013 ; Wang et al, 2011 ; Baidya and Ahad, 2016 ; Pacheck et al, 2016 ; Rutkove et al, 2017 ; Schooling et al, 2020 ; de Cardoner et al, 2021 ; Luo et al, 2022 ; Schrunder et al, 2022 ).…”

“…As this technique is sensitive to muscle morphological, electrical properties and the electrode configuration ( Schrunder et al, 2022 ), it is then necessary to identify the impact of these factors and to explore approaches to minimize their effects. Computational modeling that incorporates detailed finite element models has been proven capable in studying the biophysical mechanisms of EIM and optimize the design of electrode arrangements ( Ahad and Rutkove, 2009 ; Jafarpoor et al, 2011 , 2013 ; Wang et al, 2011 ; Baidya and Ahad, 2016 ; Pacheck et al, 2016 ; Rutkove et al, 2017 ; Schooling et al, 2020 ; de Cardoner et al, 2021 ; Luo et al, 2022 ; Schrunder et al, 2022 ). Wang et al (2011) utilized a finite element model to investigate the correlation between changes in surface impedance and electrical properties of the muscle, offering valuable insights into the biophysical mechanisms underlying the EIM.…”

“… Wang et al (2011) utilized a finite element model to investigate the correlation between changes in surface impedance and electrical properties of the muscle, offering valuable insights into the biophysical mechanisms underlying the EIM. Schrunder et al (2022) simulated muscle tissues and impacts from anthropometric variations and EIM electrode placements. Baidya and Ahad (2016) applied a finite element model to determine an optimized EIM electrode configuration which could balance both stability and sensitivity.…”

“…Computational Model Finite element model Ahad and Rutkove, 2009;Wang et al, 2011;Jafarpoor et al, 2011;Pacheck et al, 2016 • Finite element models have been demonstrated as a capable method to investigate the biophysical mechanisms of EIM in various diseases. Jafarpoor et al, 2013;Baidya and Ahad, 2016;Rutkove et al, 2017;de Cardoner et al, 2021;Schooling et al, 2020;Luo et al, 2022;Schrunder et al, 2022 • Finite element analysis provides a basic methodology to optimize electrode configuration for EIM Machine learning model Srivastava et al, 2012;Pandeya et al, 2022 • Combining biomarkers from EIM or other assessments could enhance the diagnostic performance of machine learning models. Kapur et al, 2018a,b;Pandeya et al, 2021a,b;Cheng et al, 2022 • Combining EIM and other biomarkers through machine learning can improve diagnostic accuracy for estimating muscle function parameters.…”

Feasibility Analysis and Implementation of Head-Mounted Electrical Impedance Respiratory Monitoring

“…The head was presented as an overall ellipsoidal structure (a = 16 cm, b = 13 cm, c = 13 cm), where the skin thickness was 2 mm, the skull thickness was 1 cm, and the filling within the skull was assumed to be a homogeneous medium. The dielectric properties of each tissue, namely relative permittivity and conductivity, were obtained from published literature and online reference databases [ 28 , 29 , 30 ], and their specific parameters were set as shown in Table 1 .…”