Kara Sadik, Güven Ayşegül
Erciyes University, Department of Electrical and Electronics Engineering, 38039 Kayseri, Turkey.
Comput Biol Med. 2007 Jan;37(1):77-82. doi: 10.1016/j.compbiomed.2005.10.005. Epub 2005 Dec 6.
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.
在本研究中，利用人工神经网络（ANN）对172名健康受试者和148名患病受试者在视觉刺激后跨视网膜细胞诱发的图形视网膜电图（PERG）信号进行了分析。采用人工神经网络对PERG信号进行分析，以区分健康眼和患病眼。所研究的监督网络是竞争学习矢量量化网络。设计的分类结构灵敏度约为94%，特异性为90.32%，假阴性率为5.94%，假阳性率为9.67%，计算得出的正确分类率为92%。测试结果与医生直接诊断得出的预期结果相符。最终的好处是帮助医生毫不犹豫地做出最终决定。
