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.
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Huynh, H.T., Won, Y. (2019). Application of Regularized Online Sequential Learning for Glucose Correction. In: Hameurlain, A., Wagner, R., Dang, T. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XLI. Lecture Notes in Computer Science(), vol 11390. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-58808-6_7
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