Hematocrit estimation using online sequential extreme learning machine

Hieu Trung Huynh, Quan Dac Ho
Author affiliations

Authors

  • Hieu Trung Huynh Industrial University of Ho Chi Minh city
  • Quan Dac Ho Faculty of Information Technology, Industrial University of Ho Chi Minh city

DOI:

https://doi.org/10.15625/1813-9663/29/3/2750

Keywords:

Hematocrit, neural network, extreme learning machine, online sequential training, glucose measurement.

Abstract

Hematocrit (HCT) is the volume percentage of red blood cells in the whole blood. This is the most highly influencing factor in glucose measurement using handheld devices. In this paper, we present a new approach to estimate hematocrit from the transduced current curve which is produced by chemical reaction on electrochemical biosensors used in glucose measurement. Our method utilizes the single-hidden layer feedforward neural network trained by online sequential extreme learning machine. The experimental results are given to show  high level of accuracy of the proposed method.

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Published

02-07-2013

How to Cite

[1]
H. T. Huynh and Q. D. Ho, “Hematocrit estimation using online sequential extreme learning machine”, JCC, vol. 29, no. 3, pp. 277–284, Jul. 2013.

Issue

Section

Computer Science