Artificial Odor Discrimination System using electronic nose and neural networks for the identification of urinary tract infection

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Kodogiannis, Vassilis ; Lygouras, John N. ; Tarczynski, Andrzej ; Chowdrey, Hardial S.

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 has been used to detect Urinary Tract Infection from 45 suspected cases that were sent for analysis in a UK Public Health Registry. These samples were analysed by incubation in a volatile generation test tube system for 4-5h. Two issues are being addressed, including the implementation of an advanced neural network, based on a modified Expectation Maximisation scheme that incorporates a dynamic structure methodology and the concept of a fusion of multiple classifiers dedicated to specific feature parameters. This study has shown the potential for early detection of microbial ontaminants in urine samples using electronic nose technology.
  • References (21)
    21 references, page 1 of 3

    [1] V. Moret-Bonillo, “Integration of data information and knowledge in intelligent patient monitoring,” Expert Syst. Appl., vol. 15, pp. 155-163, 1998.

    [2] M. Phillips, “Method for the collection and assay of volatile organic compounds in breath,” Anal. Biochem., vol. 247, pp. 272-278, 1997.

    [3] S. Ampuero and J. Bosset, “The electronic nose applied to dairy products: A review,” Sens. Actuators B, vol. 94, pp. 1-12, 2003.

    [4] W. Gopel, “Chemical imaging: I. Concepts and visions for electronic and bioelectronic noses,” Sens. Actuators B, vol. 52, pp. 125-142, 1998.

    [5] K. Persaud, A. M. Pisanelli, P. Evans, and P. Travers, “Monitoring urinary tract infections and bacterial vaginosis,” Sens. Actuators B, vol. 116, pp. 116-120, 2006.

    [6] A. K. Pavlou, V. S. Kodogiannis, and A. P. F. Turner, “Intelligent classification of bacterial clinical isolates in vitro, using electronic noses,” in Proc. Int. Conf. Neural Netw. Expert Syst. Med. HealthCare, 2001, pp. 231-237.

    [7] A. K. Pavlou, N. Magan, D. Sharp, J. Brown, H. Barr, and A. P. F. Turner, “An intelligent rapid odour recognition model in discrimination of Helicobacter pylori and other gastroesophageal isolates in vitro,” Biosens. Bioelectron., vol. 15, pp. 333-342, 2000.

    [8] R. Fend, C. Bessant, A. J. Williams, and A. C. Woodman, “Monitoring haemodialysis using electronic nose and chemometrics,” Biosens. Bioelectron., vol. 19, no. 12, pp. 1581-1590, 2004.

    [9] V. S. Kodogiannis, A. K. Pavlou, P. Chountas, and A.P. F. Turner, “Evolutionary computing techniques for diagnosis of urinary tract infections in vivo, using gas sensors,” in Neural Computing and Soft Computing (Advance in Soft Computing), New York, 2003, pp. 474-479.

    [10] M. Sugeno, “Fuzzy measures and fuzzy integrals: A survey,” in Fuzzy Automata and Decision Processes, M. M. Gupta, G. N. Saridis, and B. R. Gaines, Eds. Amsterdam, The Netherlands: North Holland, 1977, pp. 89-102.

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