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.
CITATION STYLE
Schetinin, V., Schult, J., Scheidt, B., & Kuriakin, V. (2003). Learning multi-class neural-network models from electroencephalograms. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 2773 PART 1, pp. 155–162). Springer Verlag. https://doi.org/10.1007/978-3-540-45224-9_23
Mendeley helps you to discover research relevant for your work.