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 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.
Slater
J D
JD
Department of Neurology, University of Miami School of Medicine, FL 33136.
Wu
F Y
FY
Honig
L S
LS
Ramsay
R E
RE
Morgan
R
R
D000161:Acoustic Stimulation; D001921:Brain / Q000503:physiopathology*; D004569:Electroencephalography; D005072:Evoked Potentials, Auditory / Q000502:physiology*; D006801:Humans; D009103:Multiple Sclerosis / Q000503:physiopathology*; D016571:Neural Networks, Computer