A Resistor Network Optimization Algorithm Enabling Synthetic Bioimpedance Generation for Validating Wearable Sensing Systems

Bioimpedance analysis (BIA) is a technique that allows for static or dynamic characterization of biological tissue composition. We have recently developed a wearable sensing system based on BIA for capturing local changes in tissue surrounding an intravenous (IV) catheter insertion site associated with a commonly occurring failure mode called infiltration. IV infiltration refers to the inadvertent leakage of fluid from the IV into the extravascular space rather than the vein and is a medical emergency that can lead to tissue damage or necrosis. To optimize and accelerate the development of real-time algorithms to detect changes in BIA parameters associated with the occurrence of such infiltration events, we have created a synthetic impedance generation phantom based on an optimized resistor network. Such synthetic physiological signal generators are used in other biomedical sensing applications, but have never been developed for BIA systems. In this paper, we present a custom circuit with a network of variable resistors, and an associated iterative algorithm to optimize the network's set values to meet the required impedance. The system and algorithm were characterized over the range of clinically relevant impedances (20–500
$\Omega)$
with a 50
$\mathrm{m}\Omega$
resolution. This system will accelerate future developments of real-time algorithms for IV infiltration, but can also be used for validation of wearable BIA systems for other applications such as biomechanics or cardiovascular physiology measurements.