Healthy sleep is essential for human well-being. Sleep analysis is a necessary process for the majority of sleep disorders diagnosis. In this work we propose to analyze brain activity through Electroencephalogram analysis in order to identify sleep stages variation. We focus on the classification phase. Most works in sleep stages classification are based on prior experts signal scoring which is a hard task. So many available unlabeled data remain unused. To explore more these data and enrich the study of sleep classification, we propose a hybrid approach based on learning classifier systems and artificial neural networks. The effectiveness of the proposed approach was investigated using real electroencephalography data. Good results were reached comparing to supervised learning methods usually used. The proposed approach provides also, an explicit model that could be analyzed a posteriori by experts.
['Ouanes A', 'Lilia Rejeb']
A Hybrid Approach for Sleep Stages Classification