Noninvasive Glucose Measurement Using Machine Learning and Neural Network Methods and Correlation with Heart Rate Variability. (6th January 2020)

• Record Type: Journal Article
• Authors: Gusev, Marjan; Poposka, Lidija; Spasevski, Gjoko; Kostoska, Magdalena; Koteska, Bojana; Simjanoska, Monika; Ackovska, Nevena; Stojmenski, Aleksandar; Tasic, Jurij; Trontelj, Janez
• Other Names: Llobet Eduard Academic Editor.
• Is Part Of: Journal of sensors. Volume 2020(2020)
• Publication Date: 2020-01-06
• Subjects: Detectors -- Periodicals; 681.205
• DOI: 10.1155/2020/9628281
• Languages: English
• ISSNs: 1687-725X

Abstract

Abstract : Diabetes is one of today's greatest global problems, and it is only becoming bigger. Constant measuring of blood glucose level is a prerequisite for monitoring glucose blood level and establishing diabetes treatment procedures. The usual way of glucose level measuring is by an invasive procedure that requires finger pricking with the lancet and might become painful and obeying, especially if this becomes a daily routine. In this study, we analyze noninvasive glucose measurement approaches and present several classification dimensions according to different criteria: size, invasiveness, analyzed media, sensing properties, applied method, activation type, response delay, measurement duration, and access to results. We set the focus on using machine learning and neural network methods and correlation with heart rate variability and electrocardiogram, as a new research and development trend.