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Improving the performance of electronic nose for wound infection detection using orthogonal signal correction and particle swarm optimization

Improving the performance of electronic nose for wound infection detection using orthogonal... Purpose – The purpose of this paper is to detect wound infection by electronic nose (Enose) and to improve the performance of Enose. Design/methodology/approach – Mice are used as experimental subjects. Orthogonal signal correction (OSC) is applied to preprocess the response of Enose. Radical basis function (RBF) network is used for discrimination, and the parameters in RBF are optimized by particle swarm optimization. Findings – OSC is very suitable for eliminating interference and improving the performance of Enose in wound infection detection. Research limitations/implications – Further research is required to sample wound infection dataset of human beings and to demonstrate that the Enose with proper algorithms can be used to detect wound infection. Practical implications – In this paper, Enose is used to detect wound infection, and OSC is used to improve the performance of the Enose. This widens the application area of Enose and OSC. Originality/value – The innovative concept paves the way for the application of Enose. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Sensor Review Emerald Publishing

Improving the performance of electronic nose for wound infection detection using orthogonal signal correction and particle swarm optimization

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
0260-2288
DOI
10.1108/SR-10-2012-710
Publisher site
See Article on Publisher Site

Abstract

Purpose – The purpose of this paper is to detect wound infection by electronic nose (Enose) and to improve the performance of Enose. Design/methodology/approach – Mice are used as experimental subjects. Orthogonal signal correction (OSC) is applied to preprocess the response of Enose. Radical basis function (RBF) network is used for discrimination, and the parameters in RBF are optimized by particle swarm optimization. Findings – OSC is very suitable for eliminating interference and improving the performance of Enose in wound infection detection. Research limitations/implications – Further research is required to sample wound infection dataset of human beings and to demonstrate that the Enose with proper algorithms can be used to detect wound infection. Practical implications – In this paper, Enose is used to detect wound infection, and OSC is used to improve the performance of the Enose. This widens the application area of Enose and OSC. Originality/value – The innovative concept paves the way for the application of Enose.

Journal

Sensor ReviewEmerald Publishing

Published: Aug 26, 2014

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