Training a learning vector quantization network using the pattern electroretinography signals.
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.
Kara
Sadik
S
Erciyes University, Department of Electrical and Electronics Engineering, 38039 Kayseri, Turkey. kara@erciyes.edu.tr
Güven
Ayşegül
A
D000328:Adult; D000368:Aged; D001185:Artificial Intelligence; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted; D004596:Electroretinography / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009901:Optic Nerve Diseases / Q000175:diagnosis; D012016:Reference Values; D012815:Signal Processing, Computer-Assisted