title: A neural network design for event-related potential diagnosis.
abstract: 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.
authors.affiliation: Department of Electrical & Computer Engineering, University of Miami, Coral Gables, FL 33124.
authors.forename: F Y
authors.initials: FY
authors.lastname: Wu
authors.forename: J D
authors.initials: JD
authors.lastname: Slater
authors.forename: L S
authors.initials: LS
authors.lastname: Honig
authors.forename: R E
authors.initials: RE
authors.lastname: Ramsay
mesh_terms: D003936:Diagnosis, Computer-Assisted; D004569:Electroencephalography; D005072:Evoked Potentials, Auditory; D006801:Humans; D009103:Multiple Sclerosis / Q000175:diagnosis*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D011237:Predictive Value of Tests; D011930:Reaction Time