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Application of Regularized Online Sequential Learning for Glucose Correction

  • Hieu Trung HuynhEmail author
  • Yonggwan Won
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 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.

Keywords

Hematocrit Neural network Online training Glucose correction Handheld device 

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of EngineeringVietnamese-German UniversityHo Chi Minh CityVietnam
  2. 2.Faculty of Information TechnologyIndustrial University of Ho Chi Minh CityHo Chi Minh CityVietnam
  3. 3.Department of Computer EngineeringChonnam National UniversityGwangjuKorea

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