Author(s): Slater JD, Wu FY, Honig LS, Ramsay RE, Morgan R
Neural network analysis is sensitive to subtle changes in patterns of data. We hypothesized that a disease process which can cause impairment of cortical function such as multiple sclerosis (MS) would affect the P300 cognitive evoked potential (P300) in a manner detectable by a feedforward backpropagation neural network. Such a network was trained using a learning data set consisting of 101 P300 wave forms (from 26 MS patients and 26 normal controls). The network was then used to classify 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 exposed, with an average accuracy (MS = abnormal, control = normal) of 81% for a single midline electrode, increasing to 90% using 3 midline electrodes in a jury system. Neural network analysis can be of help in distinguishing normal (control) P300 from abnormal (MS) P300.
Referred From: https://www.ncbi.nlm.nih.gov/pubmed/7510626
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