A framework for modeling bioimpedance measurements of nonhomogeneous tissues: a theoretical and simulation study

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

The respiratory rate is one of the crucial indicators for monitoring human physiological health. The purpose of this paper was to introduce a head-mounted respiratory monitoring solution based on electrical impedance sensing. Firstly, we constructed a finite element model to analyze the feasibility of using head impedance for respiratory sensing based on the physiological changes in the pharynx. After that, we developed a circuit module that could be integrated into a head-mounted respiratory monitoring device using a bioelectrical impedance sensor. Furthermore, we combined adaptive filtering and respiratory tracking algorithms to develop an app for a mobile phone. Finally, we conducted controlled experiments to verify the effectiveness of this electrical impedance sensing system for extracting respiratory rate. We found that the respiration rates measured by the head-mounted electrical impedance respiratory monitoring system were not significantly different from those of commercial respiratory monitoring devices by a paired t-test (p > 0.05). The results showed that the respiratory rates of all subjects were within the 95% confidence interval. Therefore, the head-mounted respiratory monitoring scheme proposed in this paper was able to accurately measure respiratory rate, indicating the feasibility of this solution. In addition, this respiratory monitoring scheme helps to achieve real-time continuous respiratory monitoring, which can provide new insights for personalized health monitoring.

On the measurement of skeletal muscle anisotropic permittivity property with a single cross-shaped needle insertion

Application of minimally invasive methods to enable the measurement of tissue permittivity in the neuromuscular clinic remain elusive. This paper provides a theoretical and modeling study on the measurement of the permittivity of two-dimensional anisotropic tissues such as skeletal muscle with a multi-electrode cross-shaped needle. For this, we design a novel cross-shaped needle with multiple-electrodes and analyse apparent impedance corresponding to the measured impedance. In addition, we propose three methods of estimate anisotropic muscle permittivity. Compared to existing electrical impedance-based needle methods that we have developed, the new needle design and numerical methods associated enable estimating in vivo muscle permittivity values with only a single needle insertion. Being able to measure muscle permittivity directly with a single needle insertion could open up an entirely new area of research with direct clinical application, including using these values to assist in neuromuscular diagnosis and to assess subtle effects of therapeutic intervention on muscle health.

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

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 (t _m), and inter-electrode distance (IED), on limb models over 1Hz-1 MHz 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 31.25 kHz and 62.5 kHz for the upper arm and lower leg, respectively, at 4 cm IED. The circuit model emulates variations in t _f and t _m, and simulates up to 89 times faster than FEA. The circuit model matches subjects measurements with RMS errors and , while FEA does with and .

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.