Overcoming the Bottleneck in COVID-19 Detection: A Machine-Learning Approach to Improve Accuracy of Electrochemical Impedance Spectroscopy (EIS) Detection Sensitivity

Abstract

In late 2019, coronavirus disease (COVID-19), emerged in Wuhan city, Hubei, China and claimed a large number of lives and affected billions all around the world. Accurate and scalable devices are essential for screening, diagnosis and monitoring of COVID-19 patients. In this Article, we first investigate the Electrochemical Impedance Spectroscopy (EIS) method and its limitations in detecting the positive COVID-19 cases. Afterwards, we demonstrate a machine learning model to determine the optimal parameters for designing an EIS method.