%0 Generic
%A Xia, Bin
%A Li, Qianyun
%A Jia, Jie
%A Wang, Jingyi
%A Chaudhary, Ujwal
%A Ramos-Murguialday, Ander
%A Birbaumer, Niels
%T Electrooculogram based sleep stage classification using deep belief network
%J Proceedings of the International Joint Conference on Neural Networks
%D 2015
%X In this work, we used single electrooculogram (EOG) signal to perform automatic sleep scoring. Deep belief network (DBN) and combination of DBN and Hidden Markov Models (HMM) are employed to discriminate sleep stages. Under the leave-one-out protocol, the average accuracy of DBN and DBN-HMM are 77.7% and 83.3% for all sleep stages, respectively. On the other hand, we found the EOG signal not only contribute to identify stages of Awake and rapid eye movement, also contribute to discriminate stage 2 and slow wave sleep stage.
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