La. Riquelme et al., CLASSIFICATION OF QUANTITATIVE EEG DATA BY AN ARTIFICIAL NEURAL-NETWORK - A PRELIMINARY-STUDY, Neuropsychobiology, 33(2), 1996, pp. 106-112
Previous studies from different laboratories have suggested that qEEG
could be useful for distinguishing dementia from normality. Our aims w
ere: (1) to study the ability of qEEG to distinguish dementia among di
fferent pathological conditions in ambulatory settings; (2) to compare
the ability of classical statistical analysis and of neural networks
in classifying qEEG data. We were able to obtain a multiple discrimina
nt function using a training set of patients, which classified correct
ly more than 91% of the qEEGs from an independent group of patients, w
ith less than 5% of false positives. Kohonen's neural network was trai
ned with the same set of patients. This unsupervised learning artifici
al neural network performed the classification of the independent samp
le with an accuracy comparable to that of the multiple discriminant fu
nction. Our results suggest that the use of unsupervised learning algo
rithms could be an interesting alternative in the classification of da
ta obtained from psychiatric patients where definition of their clinic
al profile is not always a simple task.