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• W1617232655 abstract "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." @default.
• W1617232655 created "2016-06-24" @default.
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• W1617232655 date "2015-07-01" @default.
• W1617232655 modified "2023-09-25" @default.
• W1617232655 title "Electrooculogram based sleep stage classification using deep belief network" @default.
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• W1617232655 doi "https://doi.org/10.1109/ijcnn.2015.7280775" @default.
• W1617232655 hasPublicationYear "2015" @default.
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