Name
Title | ||
|---|---|---|
Artificial odor discrimination system using electronic nose and neural networks for the identification of urinary tract infection. |
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 has been used to detect urinary tract infection from 45 suspected cases that were sent for analysis in a U.K. Public Health Registry. These samples were analyzed by incubation in a volatile generation test tube system for 4-5 h. Two issues are being addressed, including the implementation of an advanced neural network, based on a modified expectation maximization 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 contaminants in urine samples using electronic nose technology. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/TITB.2008.917928 | IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society |
Keywords | Field | DocType |
electronic nose,dynamic structure methodology,gas sensing technology,artificial odor discrimination,expectation-maximisation algorithm,neural networks,chemical analysis,medical signal detection,diseases,neural networks (nns),pattern recognition,time 4 hr to 5 hr,uk public health registry,microbial contaminants,microorganisms,expectation maximization scheme,multiple classifiers,electronic nose technology,biochemistry,test tube system,urinary tract infection,microbial analysis,pattern recognition method,medical computing,somatosensory phenomena,patient diagnosis,neural nets | Electronic nose,Early detection,Odor discrimination,Computer science,Artificial intelligence,Artificial neural network,Machine learning | Journal |
Volume | Issue | ISSN |
12 | 6 | 1558-0032 |
Citations | PageRank | References |
13 | 1.07 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vassilis S. Kodogiannis | 1 | 272 | 35.17 |
| J. N. Lygouras | 2 | 53 | 5.33 |
| A. Tarczynski | 3 | 51 | 7.01 |
| H. S. Chowdrey | 4 | 14 | 1.43 |