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Hematocrit estimation using online sequential extreme learning machine

Issue title: Frontiers in Biomedical Engineering and Biotechnology – Proceedings of the 4th International Conference on Biomedical Engineering and Biotechnology, 18–21 August 2015, Shanghai, China

Guest editors: Feng Liu, Dong-Hoon Lee, Ricardo Lagoa and Sandeep Kumar

Article type: Research Article

Authors: Huynh, Hieu Trunga | Won, Yonggwanb; * | Kim, Jinsulb

Affiliations: [a] Faculty of Information Technology, Industrial University of Ho Chi Minh City, Viet Nam | [b] Department of Computer Engineering, Chonnam National University, Gwangju 500-757, Korea

Correspondence: [*] Address for correspondence: Yonggwan Won, Department of Computer Engineering, Chonnam National University, Korea. Tel.: 82-62-530-1804; Fax: 82-62-530-1759; E-mail: [email protected].

Keywords: hematocrit, extreme learning machine, online training, OS-ELM, neural network

DOI: 10.3233/BME-151507

Journal: Bio-Medical Materials and Engineering, vol. 26, no. s1, pp. S2025-S2032, 2015

Published: 2015
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Hematocrit is a blood test that is defined as the volume percentage of red blood cells in the whole blood. It is one of the important indicators for clinical decision making and the most effective factor in glucose measurement using handheld devices. In this paper, a method for hematocrit estimation that is based upon the transduced current curve and the neural network is presented. The salient points of this method are that (1) the neural network is trained by the online sequential extreme learning machine (OS-ELM) in which the devices can be still trained with new samples during the using process and (2) the extended features are used to reduce the number of current points which can save the battery power of devices and speed up the measurement process.

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