RT Conference Proceedings
T1 Electrooculogram based sleep stage classification using deep belief network
A1 Xia, Bin
A1 Li, Qianyun
A1 Jia, Jie
A1 Wang, Jingyi
A1 Chaudhary, Ujwal
A1 Ramos-Murguialday, Ander
A1 Birbaumer, Niels
AB 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.
PB Institute of Electrical and Electronics Engineers Inc.
SN 9781479919604
SN 9781479919604
SN 9781479919604
SN 9781479919604
YR 2015
FD 2015-09-28
LA eng
NO Xia , B , Li , Q , Jia , J , Wang , J , Chaudhary , U , Ramos-Murguialday , A & Birbaumer , N 2015 , Electrooculogram based sleep stage classification using deep belief network . in 2015 International Joint Conference on Neural Networks, IJCNN 2015 . , 7280775 , Proceedings of the International Joint Conference on Neural Networks , vol. 2015-September , Institute of Electrical and Electronics Engineers Inc. , International Joint Conference on Neural Networks, IJCNN 2015 , Killarney , Ireland , 12/07/15 . https://doi.org/10.1109/IJCNN.2015.7280775
NO conference
NO Publisher Copyright: © 2015 IEEE.
DS TECNALIA Publications
RD 26 jul 2024