Sleep stages classification by hierarchical artificial neural networks

The goal of our work is to provide an automatic analysis and decision tool for sleep stages classification based on an artificial neural networks (ANN). The first difficulty lies in choosing the physiological signals representation and in particular the electroencephalogram (EEG). Once the representation adopted, the next step is to design the optimal neural network determined by a learning and validation process of data from a set of sleep records. We studied several configurations of conventional ANN giving results varying from 62 to 71 %, then we proposed a new hierarchical configuration, which gives a rate of 74% correct classification for six stages. These results lead us to further explore this issue at the representation and design of ANNs to improve the performance of our tool. (C) 2012 Elsevier Masson SAS. All rights reserved.