Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/7214
Title: Noninvasive Glucose Measurement Using Machine Learning and Neural Network Methods and Correlation with Heart Rate Variability
Authors: Gushev, Marjan 
Poposka, Lidija 
Spasevski, Gjoko
Kostoska, Magdalena 
Koteska, Bojana 
Simjanoska, Monika
Ackovska, Nevena 
Stojmenski, Aleksandar
Tasic, Jurij
Trontelj, Janez
Keywords: Noninvasive Glucose Measurement,
Heart Rate Variability
Issue Date: Sep-2019
Publisher: Hindawi
Journal: Journal of Sensors
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.
URI: http://hdl.handle.net/20.500.12188/7214
DOI: https://doi.org/10.1155/2020/9628281
Appears in Collections:Faculty of Medicine: Journal Articles

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