Data Processing for Noninvasive Continuous Glucose Monitoring with a Multisensor Device

Background: Impedance spectroscopy has been shown to be a candidate for noninvasive continuous glucose monitoring in humans. However, in addition to glucose, other factors also have effects on impedance characteristics of the skin and underlying tissue. Method: Impedance spectra were summarized through a principal component analysis and relevant variables were identified with Akaike’s information criterion. In order to model blood glucose, a linear least-squares model was used. A Monte Carlo simulation was applied to examine the effects of personalizing models. Results: The principal component analysis was able to identify two major effects in the impedance spectra: a blood glucose-related process and an equilibration process related to moisturization of the skin and underlying tissue. With a global linear least-squares model, a coefficient of determination ( R 2 ) of 0.60 was achieved, whereas the personalized model reached an R 2 of 0.71. The Monte Carlo simulation proved a significant advantage of personalized models over global models. Conclusion: A principal component analysis is useful for extracting glucose-related effects in the impedance spectra of human skin. A linear global model based on Solianis Multisensor data yields a good predictive power for blood glucose estimation. However, a personalized linear model still has greater predictive power.