TOWARDS AUTOMATED SLEEP CLASSIFICATION IN INFANTS USING SYMBOLIC AND SUBSYMBOLIC APPROACHES

The paper addresses the problem of automatic sleep classification. A s pecial effort is made to find a method of extracting reasonable descri ptions of the individual sleep stages from sample measurements of EGG, EMG, EOG, etc., and from a classification of these measurements provi ded by an expert. The method should satisfy three requirements: classi fication accuracy, interpretability of the results, and the ability to select the relevant and discard the irrelevant variables. The solutio n suggested in this paper consists of a combination of the subsymbolic algorithm LVQ with the symbolic decision tree generator ID3. Results demonstrating the feasibility and utility of our approach are also pre sented.