Development of machine learning techniques for non-invasive blood glucose sensor - Jiang, Jiaxin (2019)
Diabetes mellitus is a common chronic disease with more than 8% of the world population suffered from it. Millions of diabetics have to undergo painful and grueling invasive blood glucose testing several times a day to monitor their blood glucose level. In order to solve this problem, scientists all over the world have spent a great effort on the research of non-invasive blood glucose measurement and proposed a variety of methods, but none of them has been proven to be reliable in clinical practice. This dissertation attempts to develop a microwave-based non-invasive blood glucose measurement method with Machine Learning technique, in order to increase the accuracy of blood glucose level estimation. In this work, by creating a human earlobe biological model and simulating it in CST microwave studio, the dielectric behavior of blood with the variation of glucose concentration is investigated in a high frequency range. Applying broadband sweep to the model, a feasible operating region is found at the frequency range of 60-62 GHz. The result of the simulation is validated by an in-vitro experiment conducted on artificial blood plasma in the electromagnetic environment, and the data obtained is used for training Machine Learning models. By comparing the performances of the models, the SVM classifier is selected to be the best solution in this case. The work in this dissertation has the potential to be used in blood glucose monitoring to reduce their pain and warn them of upcoming life-threatening conditions.