Enhancing the precision of impedance measurement from 5 kHz to 1 MHz through self-identification of parasitic parameters.

Abstract

Objective: Electrical impedance tomography (EIT) generates cross-sectional images through non-invasive impedance measurements from surface electrodes. While impedance above 200 kHz can reveal intracellular properties, most existing EIT images are published at frequencies below 200 kHz. When frequencies exceed 200 kHz, the accuracy of impedance measurements declines due to the influence of distributed circuit parameters such as parasitic capacitance, on-resistance of switch and the series inductance, with a more significant impact on larger impedance. To overcome this limitation, this paper proposes an approach for precision impedance measurement through self-identification of distributed parameter.

Approach: Firstly, the distributed circuit parameters are identified via correction measurements of precision resistances in the frequency range from 5 kHz to 1 MHz; then, the circuit is accurately modeled; finally, transfer impedance measurements during imaging process are corrected using the established circuit model.

Main results: The distributed circuit parameter self-identification method was verified through a goodness-of-fit test, confirming the consistency between the model's predicted values and the actual values of the component. The test results indicate that at 1 MHz, the relative residuals follow a right-skewed distribution with an average value of 0.08%, which demonstrates high model accuracy. At 1 MHz, the measurement relative error after correction for the 499 Ω precision resistor is reduced by 12.01%, and for the 56 pF precision capacitor, the relative error after correction is 0.46%.

Significance: The proposed method can extend the frequency range of EIT and other impedance technologies from below 200 kHz to up to 1 MHz, while ensuring good measurement accuracy.