The Kullback-Leibler nearest neighbor rule provides a reliable method for automatic classification of EEG states, such as depth of anesthesia, using a small number of labeled samples.
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.
Gersch et al. (Fri,) studied this question.