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Traditionally the analysis of sleep has used two distinct manual EEG analysis methods: one for general structure, the other for short time-scale events. Both methods suffer from high levels of inter-expert variability. In this paper we present a system which uses a neural network classifier to analyse each second of sleep. Post-processing techniques are described which result in outputs which mimic both of the traditional manual analysis methods. This combination of methods results in a comprehensive sleep analysis system providing information on both the macro and microstructure of sleep. Our results show that it is possible to use a combined approach to sleep analysis and that there is strong correlation between expert scoring and the post-processed neural network output.

Original publication




Conference paper

Publication Date





1608 - 1611