Automatic Classification of Electroencephalograms: Kullback-Leibler Nearest Neighbor Rules
- 13 July 1979
- journal article
- research article
- Published by American Association for the Advancement of Science (AAAS) in Science
- Vol. 205 (4402) , 193-195
- https://doi.org/10.1126/science.451587
Abstract
A prototypic problem in screening of electroencephalograms in the automatic classification of stationary electroencephalogram time series is treated here by the Kullback-Leibler nearest neighbor rule approach. In that problem, the category or state of an individual is classified by comparison of his or her electroencephalogram with those taken from other individuals in the alternative categories. The Kullback-Leibler nearest neighbor classification rules yield a statistically reliable estimate of the smallest possible probability of electroencephalogram misclassification with a relatively small number of labeled sample electroencephalograms. The automatic classification of anesthesia levels L1 and L3, respectively the anesthesia levels insufficient and sufficient for deep surgery, is treated by machine computation on the electroencephalogram alone.This publication has 10 references indexed in Scilit:
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