NEURAL-NETWORK ANALYSIS OF THE P300 EVENT-RELATED POTENTIAL IN MULTIPLE-SCLEROSIS

Neural network analysis is sensitive to subtle changes in patterns of data. We hypothesized that a disease process which can cause impairmen t of cortical function such as multiple sclerosis (MS) would affect th e P300 cognitive evoked potential (P300) in a manner detectable by a f eedforward backpropagation neural network. Such a network was trained using a learning data set consisting of 101 P300 wave forms (from 26 M S patients and 26 normal controls). The network was then used to class ify a randomly selected test data set of 20 studies (2 studies each of 5 MS patients and 5 controls) to which it had not been previously exp osed, with an average accuracy (MS = abnormal, control = normal) of 81 % for a single midline electrode, increasing to 90% using 3 midline el ectrodes in a jury system. Neural network analysis can be of help in d istinguishing normal (control) P300 from abnormal (MS) P300.