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Electronic nose: clinical diagnosis based on soft computing methodologies

Kodogiannis, Vassilis and Chountas, Panagiotis and Pavlou, A.K. and Petrounias, Ilias and Chowdrey, Hardial S. and Temponi, Cecelia (2002) Electronic nose: clinical diagnosis based on soft computing methodologies. In: Proceedings of the IEEE International Symposium Intelligent Systems: Methodology, Models, Applications in Emerging Technologies IS2002, 10-12 Sept.2002, Varna, Bulgaria. IEEE Computer Society, USA, pp. 254-259. ISBN 0780371348

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Official URL: http://dx.doi.org/10.1109/IS.2002.1044264

Abstract

Recently, the use of smell in clinical diagnosis has been rediscovered due to major advances in odour sensing technology and artificial intelligence. It was well known in the past that a number of infectious or metabolic diseases could liberate specific odours characteristic of the disease stage and among others, urine volatile compounds have been identified as possible diagnostic markers. A newly developed electronic nose based on chemoresistive sensors has been employed to identify in vitro 13 bacterial clinical isolates, collected from patients diagnosed with urinary tract infections, gastrointestinal and respiratory infections, and in vivo urine samples from patients with suspected uncomplicated UTI who were scheduled for microbiological analysis in a UK health laboratory environment. An intelligent model consisting of an odour generation mechanism, and a classifier system based a neural networks, genetic algorithms, and multivariate techniques such as principle components analysis and discriminant function analysis-cross validation. The experimental results confirm the validility of the presented methods.

Item Type:Book Section
Uncontrolled Keywords:Neural networks, genetic algorithms, electronic noses, microbial analysis
Research Community:University of Westminster > Electronics and Computer Science, School of
ID Code:822
Deposited On:05 Dec 2005
Last Modified:11 Aug 2010 15:29

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