Parasitic Capacitances Estimation of an Electrical Impedance Tomography Data Acquisition System by Bayesian Inference

Electrical Impedance Tomography (EIT) is a technique that produces an image from a current injection inside the vessel. Since the problem is severely ill-posed, errors and noise can lead to instabilities in the image. One of these errors’ primary sources is the parasitic capacitances that shunt the signal to the ground. These non-idealities are present in integrated circuits, cables, printed circuit boards, and others. Thus, it is crucial to determine the quantity of these parasitic capacitances present in the circuit to design methods to avoid these problems. Given these reasons, this work aims to estimate the output impedance and parasitic capacitances from the data acquisition system. For that, the Maximum a Posteriori (MAP) Estimate, and the Monte Carlo Markov Chain (MCMC) is used. The method is validated and utilized to estimate the unknown quantities’ input impedance from a developed system. © 2021 Elsevier Ltd