Abstract
Electronic Nose based ENT bacteria identification in hospital environment is a classical and challenging problem of classification. In this paper an electronic nose (e-nose), comprising a hybrid array of 12 tin oxide sensors (SnO2) and 6 conducting polymer sensors has been used to identify three species of bacteria, Escherichia coli (E. coli), Staphylococcus aureus (S. aureus), and Pseudomonas aeruginosa (P. aeruginosa) responsible for ear nose and throat (ENT) infections when collected as swab sample from infected patients and kept in ISO agar solution in the hospital environment. In the next stage a sub-classification technique has been developed for the classification of two different species of S. aureus, namely Methicillin-Resistant S. aureus (MRSA) and Methicillin Susceptible S. aureus (MSSA). An innovative Intelligent Bayes Classifier (IBC) based on "Baye's theorem" and "maximum probability rule" was developed and investigated for these three main groups of ENT bacteria. Along with the IBC three other supervised classifiers (namely, Multilayer Perceptron (MLP), Probabilistic neural network (PNN), and Radial Basis Function Network (RBFN)) were used to classify the three main bacteria classes. A comparative evaluation of the classifiers was conducted for this application. IBC outperformed MLP, PNN and RBFN. The best results suggest that we are able to identify and classify three bacteria main classes with up to 100% accuracy rate using IBC. We have also achieved 100% classification accuracy for the classification of MRSA and MSSA samples with IBC. We can conclude that this study proves that IBC based e-nose can provide very strong and rapid solution for the identification of ENT infections in hospital environment.
Highlights
An electronic nose (e-nose) is an instrument that has been developed as a simplified "electronic" model of the human olfactory system
The system consists of 12 tin oxide sensors (SnO2) and 6 conducting polymer sensors configured into a hybrid array has been used to identify three species of bacteria, Escherichia coli (E. coli), Staphylococcus aureus (S. aureus), and Pseudomonas aeruginosa (P. aeruginosa) responsible for ear nose and throat (ENT) infections when collected as swab sample from infected patients and kept in ISO agar solution in the hospital environment
We aimed to keep the consistency of this testing method along with artificial neural network (ANN) based testing methods. Advantage of this method is the tested data is virtually behaving like an unknown data. Following this approach we achieved 100% for the overall classification of three species of bacteria, Escherichia coli (E. coli), Staphylococcus aureus (S. aureus), and Pseudomonas aeruginosa (P. aeruginosa) responsible for ear nose and throat (ENT) infections
Summary
An electronic nose (e-nose) is an instrument that has been developed as a simplified "electronic" model of the human olfactory system. The sensation of flavour is due to three main chemoreceptor systems. These are gustation (sense of taste by tongue), olfaction (sense of smell by nose) and trigeminal (sense of irritation of trigeminal receptors). Receptors for the trigeminal sense are located in the mucous membranes and in the skin, they respond to certain volatile chemicals and it is thought to be especially important in the detection of irritants and chemically reactive species. In the perception of flavour all three chemoreceptor systems are involved but olfaction plays by far the greatest role. An electronic nose (e-nose) is an instrument that is designed to detect and discriminate dif-
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