Background interference elimination in wound infection detection by electronic nose based on reference vector-based Independent Component Analysis

Abstract

Background interference is serious and widespread problem in wound infection detection by electronic nose (ENose). When mice are used as experimental subjects, the background interference, i.e., the odor of the mice themselves, is very strong and useful information is often buried in it. A new method of eliminating the background interference and detecting wound infection, based on an ENose in cooperation with reference vector-based Independent Component Analysis (ICA) denoising algorithm is proposed. It employs ICA to decompose each signal of the sensor array and extract the independent components and then discriminates the useful sources and Background interference through the Correlation with the reference Vector. The independent components of which the background interference had been eliminated are used as the inputs of Radial Basis Function (RBF) network for discrimination. The result shows that this method is effective and practical for background interference elimination in the detection of wound infection by ENose. © 2012 Asian Network for Scientific Information.