Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-24T20:38:53.163Z Has data issue: false hasContentIssue false
  • English
  • Français

Classification of Ear, Nose, and Throat Bacteria Using a Neural-Network-Based Electronic Nose

Published online by Cambridge University Press:  31 January 2011

Ritaban Dutta ,
Julian W. Gardner  and
Evor L. Hines
Get access

Abstract

This article describes the use of an electronic nose (the Cyranose 320) to sense and identify three species of bacteria responsible for ear, nose, and throat (ENT) infections. Gathered data were a complex mixture of different chemical compounds. An innovative approach for classifying the bacteria data was investigated by using a combination of several clustering algorithms. The best results suggest that the three classes of bacteria examined can be predicted with up to 98% accuracy, allowing more precise diagnosis of ENT infection in patients. This type of bacteria data analysis and feature extraction is difficult, but it can be concluded that combined use of the analysis methods described here can solve the feature extraction problem with very complex data and enhance the performance of electronic noses.

Keywords

bacteria analysiselectronic nosesENT infectionsfuzzy c-meansmultilayer perceptronprincipal component analysisprobabilistic neural networksradial basis function networksself-organizing map networks.
Type
Research Article
Information
MRS Bulletin , Volume 29 , Issue 10: Novel Materials and Applications of Electronic Noses and Tongues , October 2004 , pp. 709 - 713
Copyright
Copyright © Materials Research Society 2004

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1Pearce, T.C., Schiffman, S.S., Nagle, H T., and Gardner, J.W., eds., Handbook of Machine Olfaction: Electronic Nose Technology, 1st ed. (Wiley-VCH, Weinheim, Germany, 2003).Google Scholar
2Cyrano Sciences Home Page, www.cyranosciences.com (accessed September 2004).Google Scholar
3Dutta, R., Hines, E.L., Gardner, J.W., and Boilot, P., Biomed. Eng. Online 1 (4) (2002).Google Scholar
4Gardner, J.W., Craven, M., Dow, C.S., and Hines, E.L., Meas. Sci. Technol. 9 (1998) p. 120.CrossRefGoogle Scholar
5Gardner, J.W. and Bartlett, P.N., Electronic Noses: Principles and Applications (Oxford University Press, UK, 1999).CrossRefGoogle Scholar
6Shin, H.W., Llobet, E., Gardner, J.W., Hines, E L., and Dow, C.S., IEE Proc. Sci. Meas. Technol. 147 (2000) p. 158.CrossRefGoogle Scholar
7The MathWorks Home Page, www.mathworks.com (accessed September 2004).Google Scholar
8Gardner, J.W., Sens. Actuators, B 4 (1991) p. 108.Google Scholar
9Jang, J.S.R., Sun, C.T., and Mizutani, E., Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence (Prentice Hall, Upper Saddle River, NJ, 1997).Google Scholar
10Kohonen, T., Self-Organising and Associative Memory, 2nd ed. (Springer-Verlag, Berlin, 1987).Google Scholar