Electrooculogram based sleep stage classification using deep belief network

Bin Xia; Qianyun Li; Jie Jia; Jingyi Wang; Ujwal Chaudhary; Ander Ramos-Murguialday; Niels Birbaumer

doi:10.1109/IJCNN.2015.7280775

Electrooculogram based sleep stage classification using deep belief network

Bin Xia, Qianyun Li, Jie Jia, Jingyi Wang, Ujwal Chaudhary, Ander Ramos-Murguialday, Niels Birbaumer

Neuroengineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

21 Citations (Scopus)

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.

Original language	English
Title of host publication	2015 International Joint Conference on Neural Networks, IJCNN 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781479919604, 9781479919604, 9781479919604, 9781479919604
DOIs	https://doi.org/10.1109/IJCNN.2015.7280775
Publication status	Published - 28 Sept 2015
Event	International Joint Conference on Neural Networks, IJCNN 2015 - Killarney, Ireland Duration: 12 Jul 2015 → 17 Jul 2015

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
Volume	2015-September

Conference

Conference	International Joint Conference on Neural Networks, IJCNN 2015
Country/Territory	Ireland
City	Killarney
Period	12/07/15 → 17/07/15

Keywords

EOG
Hidden Markov Models
automatic sleep stage classification
deep belief network

Access to Document

10.1109/IJCNN.2015.7280775

Cite this

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 Article 7280775 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2015-September). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2015.7280775

@inproceedings{45356f804c5b4006a659e8910465b5d7,

title = "Electrooculogram based sleep stage classification using deep belief network",

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.",

keywords = "EOG, Hidden Markov Models, automatic sleep stage classification, deep belief network",

author = "Bin Xia and Qianyun Li and Jie Jia and Jingyi Wang and Ujwal Chaudhary and Ander Ramos-Murguialday and Niels Birbaumer",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; International Joint Conference on Neural Networks, IJCNN 2015 ; Conference date: 12-07-2015 Through 17-07-2015",

year = "2015",

month = sep,

day = "28",

doi = "10.1109/IJCNN.2015.7280775",

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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

Electrooculogram based sleep stage classification using deep belief network. / Xia, Bin; Li, Qianyun; Jia, Jie et al.
2015 International Joint Conference on Neural Networks, IJCNN 2015. Institute of Electrical and Electronics Engineers Inc., 2015. 7280775 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2015-September).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

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.

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M3 - Conference contribution

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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

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ER -

Xia B, Li Q, Jia J, Wang J, Chaudhary U, Ramos-Murguialday A et al. Electrooculogram based sleep stage classification using deep belief network. In 2015 International Joint Conference on Neural Networks, IJCNN 2015. Institute of Electrical and Electronics Engineers Inc. 2015. 7280775. (Proceedings of the International Joint Conference on Neural Networks). doi: 10.1109/IJCNN.2015.7280775

Electrooculogram based sleep stage classification using deep belief network

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