Wu F Y, Slater J D, Honig L S, Ramsay R E
Department of Electrical & Computer Engineering, University of Miami, Coral Gables, FL 33124.
Comput Biol Med. 1993 May;23(3):251-64. doi: 10.1016/0010-4825(93)90024-u.
Many diseases resulting in neuropsychological impairment show abnormalities by EEG (electroencephalogram) tests. However, EEG analysis is complicated by a wide spectrum of normal patterns. Cerebral evoked potentials are stimulus-induced, averaged EEG potentials that have been found useful in patients with dementing illness. A recent report using the P300 auditory evoked potential shows that simple latency and waveform criteria result in classification accuracy of 65% for multiple sclerosis (MS) patients versus 91% for control subjects. Analysis on the same data set was performed using an artificial neural network (ANN) and a nearest neighbor (NN) classifier. An ANN classifier demonstrated a classification accuracy of 75% versus 87% on MS and control subject groups. Thus prediction accuracy was improved on average, compared with that obtained by NN classifiers or P300 statistical analysis. The classification strategy discovered by a trained ANN was analyzed by a weight pattern analysis method and compared with the P300 latency criteria.
许多导致神经心理障碍的疾病通过脑电图（EEG）测试会显示出异常。然而，脑电图分析因存在广泛的正常模式而变得复杂。脑诱发电位是由刺激诱发的平均脑电图电位，已发现其对患有痴呆症的患者有用。最近一份使用P300听觉诱发电位的报告显示，简单的潜伏期和波形标准对多发性硬化症（MS）患者的分类准确率为65%，而对对照组受试者的分类准确率为91%。使用人工神经网络（ANN）和最近邻（NN）分类器对同一数据集进行了分析。ANN分类器在MS组和对照组受试者上的分类准确率分别为75%和87%。因此，与通过NN分类器或P300统计分析获得的准确率相比，预测准确率平均有所提高。通过权重模式分析方法分析了经过训练的ANN发现的分类策略，并与P300潜伏期标准进行了比较。
