Data reduction in headspace analysis of blood and urine samples for robust bacterial identification.
Comput Methods Programs Biomed
; 79(3): 259-71, 2005 Sep.
Article
en En
| MEDLINE
| ID: mdl-15975689
ABSTRACT
This paper demonstrates the application of chemical headspace analysis to the problem of classifying the presence of bacteria in biomedical samples by using computational tools. Blood and urine samples of disparate forms were analysed using a Cyrano Sciences C320 electronic nose together with an Agilent 4440 Chemosensor. The high dimensional data sets resulting from these devices present computational problems for parameter estimation of discriminant models. A variety of data reduction and pattern recognition techniques were employed in an attempt to optimise the classification process. A 100% successful classification rate for the blood data from the Agilent 4440 was achieved by combining a Sammon mapping with a radial basis function neural network. In comparison a successful classification rate of 80% was achieved for the urine data from the C320 which were analysed using a novel nonlinear time series model.
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Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Espectrometría de Masas
/
Bacterias
/
Orina
/
Bacteriemia
Tipo de estudio:
Diagnostic_studies
Límite:
Humans
Idioma:
En
Revista:
Comput Methods Programs Biomed
Asunto de la revista:
INFORMATICA MEDICA
Año:
2005
Tipo del documento:
Article
País de afiliación:
Reino Unido