Non-invasive blood glucose monitoring using a hybrid technique

Abstract

Diabetes needs regular blood glucose monitoring to control it. Invasive blood glucose measuring is the current gold standard. It causes discomfort for the patient and sometimes even infections. Researchers around the world have reported different techniques to measure blood glucose levels non-invasively, but a universally acceptable method with required accuracy is not yet available. We proposed a novel approach to measure blood glucose level non-invasively using a hybrid technique combining Near InfraRed (NIR) absorption and bio-impedance measurements. We tested the methods individually first. Then Artificial Neural Network (ANN) and least squares regression were used to integrate the two methods. The combined methods showed better accuracy compared to the individual measurements. The hybrid technique developed using the linear regression models showed a superior outcome with 90% and 10% of the data points in the regions A and B of the Clarke error grid, which are considered acceptable.