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VGU RESEARCH REPOSITORY


Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/102
Title: Application of regularized online sequential learning for glucose correction
Authors: Huynh Trung Hieu 
Yonggwan Won 
Keywords: Hematocrit;Neural network;Online training;Glucose correction;Handheld device
Issue Date: 2019
Publisher: Springer Link
Series/Report no.: Lecture Notes in Computer Science;Vol. 11390
Abstract: 
Glucose measurement by using handheld devices is applied widely due to their comfortabilities. They are easy to use and can give results quickly. However, the accuracy of measurement results is affected by interferences, in which hematocrit (HCT) is one of the most highly affecting factors. In this paper, an approach for glucose correction based on the neural network is presented. The regularized online sequential learning is utilized for hematocrit estimation. The transduced current curve which is produced by the chemical reaction during glucose measurement is used as an input feature of neural network. The experimental results shown that the proposed approach is promising. page 160-171
Index & Ranking: Scopus (Q2)
URI(1): http://epub.vgu.edu.vn/handle/dlibvgu/102
DOI: 10.1007/978-3-662-58808-6_7
Appears in Collections:ARTICLE/BOOK PUBLICATION

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