Hematocrit estimation using online sequential extreme learning machine

Abstract

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.