Ве молиме користете го овој идентификатор да го цитирате или поврзете овој запис:
http://hdl.handle.net/20.500.12188/8768
Наслов: | 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 |
Issue Date: | 6-јан-2020 | Publisher: | Hindawi Limited | 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/20.500.12188/8768 | DOI: | 10.1155/2020/9628281 |
Appears in Collections: | Faculty of Computer Science and Engineering: Journal Articles |
Прикажи целосна запис
Записите во DSpace се заштитени со авторски права, со сите права задржани, освен ако не е поинаку наведено.