Affiliations: [a] Computational Intelligence Group, School of Electronics and Computer Science, University of Westminster, London, HA1 3TP, UK | [b] Manchester Business School, The University of Manchester, Manchester M15 6PB, UK | [c] Department of Electrical & Computer Engineering, Democritus University of Thrace, Xanthi, GR-67100, Greece | [x] University of Westminster, United Kingdom | [y] The University of Manchester, United Kingdom | [z] Democritus University of Thrace, Greece
Abstract: Current clinical diagnostics are based on biochemical, immunological or microbiological methods. However, these methods are operator dependent, time consuming, expensive and require special skills, and are, therefore, not suitable for point-of-care testing. Recent developments in gas-sensing technology and pattern recognition methods make electronic nose technology an interesting alternative for medical point-of-care devices. An electronic nose based on chemo-resistive sensors has been employed to identify in vitro 13 bacterial clinical isolates, collected from patients diagnosed with urinary tract infections, gastrointestinal and respiratory infections in a Public Health Laboratory environment. An intelligent unit consisting of an odour generation mechanism, rapid volatile delivery and recovery system, and a classifier system based on a novel neuro-fuzzy system has been applied in the identification and characterisation of microbial pathogens. The proposed structure constructs its initial rules by clustering, while the final fuzzy rule base is determined by competitive learning. Both error backpropagation and recursive least squares estimation, are applied to the learning scheme. The performance of the model was evaluated in terms of training performance and classification accuracies and the results show the potential for early detection of microbial contaminants in urine samples using electronic nose technology.
