Noninvasive Glucose Measurement Using Machine Learning and Neural Network Methods and Correlation with Heart Rate Variability

Gushev, Marjan; Poposka, Lidija; Spasevski, Gjoko; Kostoska, Magdalena; Koteska, Bojana; Simjanoska, Monika; Ackovska, Nevena; Stojmenski, Aleksandar; Tasic, Jurij; Trontelj, Janez

doi:https://doi.org/10.1155/2020/9628281

Noninvasive Glucose Measurement Using Machine Learning and Neural Network Methods and Correlation with Heart Rate Variability

Journal

Journal of Sensors

Date Issued

2019-09

Author(s)

Spasevski, Gjoko

Simjanoska, Monika

Stojmenski, Aleksandar

Tasic, Jurij

Trontelj, Janez

DOI

https://doi.org/10.1155/2020/9628281

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.

Subjects

Noninvasive Glucose M...

Heart Rate Variabilit...

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