  We describe a new algorithm for learning multi-class neural-network models
from large-scale clinical electroencephalograms (EEGs). This algorithm trains
hidden neurons separately to classify all the pairs of classes. To find best
pairwise classifiers, our algorithm searches for input variables which are
relevant to the classification problem. Despite patient variability and heavily
overlapping classes, a 16-class model learnt from EEGs of 65 sleeping newborns
correctly classified 80.8% of the training and 80.1% of the testing examples.
Additionally, the neural-network model provides a probabilistic interpretation
of decisions.
