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Improving sleep stage classification from electroencephalographic signals by fusion of contextual information

In this article we present a fusion architecture for the automatic classification of sleep stages. The architecture relies on time and frequency domain features which are processed by dissimilar classifiers. The initial predictions of each classifier are refined by using fusion of the prediction estimations together with temporal contextual information of the electroencephalographic signal. The experimental results showed that the proposed architecture achieved approximately 95% sleep stage classification accuracy, which corresponds to an improvement of 5% comparing to the best performing single classifier.