Non-invasive prediction of blood glucose trends during hypoglycemia

• Published in: Analytica chimica acta Vol. 1052; pp. 37 - 48
• Main Authors: Tronstad, Christian, Elvebakk, Ole, Staal, Odd Martin, Kalvøy, Håvard, Høgetveit, Jan Olav, Jenssen, Trond Geir, Birkeland, Kåre Inge, Martinsen, Ørjan G.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 04.04.2019; Elsevier BV
• Subjects: Adolescent; Adult; Blood; Blood glucose; Blood Glucose - metabolism; Clamps; Diabetes; Diabetes mellitus; Diabetes Mellitus, Type 1 - blood; Diabetes Mellitus, Type 1 - complications; Electric Impedance; Female; Glucose; Humans; Hypoglycemia; Hypoglycemia - blood; Hypoglycemia - complications; Information systems; Least-Squares Analysis; Machine learning; Male; Middle Aged; Models, Statistical; Multisensor; Neural network; Neural networks; Non invasive; Optical measurement; Predictions; Regression analysis; Regression models; Reproducibility of Results; Skin; Skin Temperature; Spectrum Analysis; Temperature measurement; Trends; Young Adult; Multisensor; Glucose; Neural network; Hypoglycemia; Machine learning; Non invasive

Over the last four decades, there has been a pursuit for a non-invasive solution for glucose measurement, but there is not yet any viable product released. Of the many sensor modalities tried, the combination of electrical and optical measurement is among the most promising for continuous measuremen...