Training a learning vector quantization network using the pattern electroretinography signals.
Comput Biol Med
; 37(1): 77-82, 2007 Jan.
Article
en En
| MEDLINE
| ID: mdl-16337176
ABSTRACT
In this study, the pattern electroretinography (PERG) signals derived from evoked potential across retinal cells of subjects after visual stimulation were analyzed using artificial neural network (ANN) with 172 healthy and 148 diseased subjects. ANN was employed to PERG signals to distinguish between healthy eye and diseased eye. Supervised network examined was a competitive learning vector quantization network. The designed classification structure has about 94% sensitivity, 90.32% specifity, 5.94% false negative, 9.67% false positive and correct classification is calculated to be 92%. Testing results were found to be compliant with the expected results that are derived from the physician's direct diagnosis. The end benefit would be to assist the physician to make the final decision without hesitation.
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Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Inteligencia Artificial
/
Electrorretinografía
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Límite:
Adult
/
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Idioma:
En
Revista:
Comput Biol Med
Año:
2007
Tipo del documento:
Article
País de afiliación:
Turquía