TY - GEN
T1 - Electrooculogram based sleep stage classification using deep belief network
AU - Xia, Bin
AU - Li, Qianyun
AU - Jia, Jie
AU - Wang, Jingyi
AU - Chaudhary, Ujwal
AU - Ramos-Murguialday, Ander
AU - Birbaumer, Niels
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/9/28
Y1 - 2015/9/28
N2 - 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.
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.
KW - EOG
KW - Hidden Markov Models
KW - automatic sleep stage classification
KW - deep belief network
UR - http://www.scopus.com/inward/record.url?scp=84951081999&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2015.7280775
DO - 10.1109/IJCNN.2015.7280775
M3 - Conference contribution
AN - SCOPUS:84951081999
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2015 International Joint Conference on Neural Networks, IJCNN 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - International Joint Conference on Neural Networks, IJCNN 2015
Y2 - 12 July 2015 through 17 July 2015
ER -