A hybrid attention temporal sequential network for sleep stage classification_Journal of Biomedical Engineering

Authors：

JIN Zheng ^1,2,3 ,  JIA Kebin ^1,2,3 , YUAN Ye ⁴

1. Faculty of Information Technology, Beijing University of Technology, Beijing 100124, P.R.China;
2. Beijing Laboratory of Advanced Information Networks, Beijing 100124, P.R.China;
3. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, P.R.China;
4. JD Intelligent Cities Research, Beijing 100190, P.R.China;

Corresponding author：

JIA Kebin, Email: kebinj@bjut.edu.cn

Keywords：

sleep stage classification; recurrent neural network; attention mechanism

DOI：

10.7507/1001-5515.202008006

Video：

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Sleep stage classification is a necessary fundamental method for the diagnosis of sleep diseases, which has attracted extensive attention in recent years. Traditional methods for sleep stage classification, such as manual marking methods and machine learning algorithms, have the limitations of low efficiency and defective generalization. Recently, deep neural networks have shown improved results by the capability of learning complex pattern in the sleep data. However, these models ignore the intra-temporal sequential information and the correlation among all channels in each segment of the sleep data. To solve these problems, a hybrid attention temporal sequential network model is proposed in this paper, choosing recurrent neural network to replace traditional convolutional neural network, and extracting temporal features of polysomnography from the perspective of time. Furthermore, intra-temporal attention mechanism and channel attention mechanism are adopted to achieve the fusion of the intra-temporal representation and the fusion of channel-correlated representation. And then, based on recurrent neural network and inter-temporal attention mechanism, this model further realized the fusion of inter-temporal contextual representation. Finally, the end-to-end automatic sleep stage classification is accomplished according to the above hybrid representation. This paper evaluates the proposed model based on two public benchmark sleep datasets downloaded from open-source website, which include a number of polysomnography. Experimental results show that the proposed model could achieve better performance compared with ten state-of-the-art baselines. The overall accuracy of sleep stage classification could reach 0.801, 0.801 and 0.717, respectively. Meanwhile, the macro average F1-scores of the proposed model could reach 0.752, 0.728 and 0.700. All experimental results could demonstrate the effectiveness of the proposed model.

Citation： JIN Zheng, JIA Kebin, YUAN Ye. A hybrid attention temporal sequential network for sleep stage classification. Journal of Biomedical Engineering, 2021, 38(2): 241-248. doi: 10.7507/1001-5515.202008006 Copy

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2.	Iber C, Ancoli-Israel S, Chesson A L, et al. The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specifications. Westchester, IL: American academy of sleep medicine, 2007.
3.	Zhang J, Yao R, Ge W, et al. Orthogonal convolutional neural networks for automatic sleep stage classification based on single-channel EEG. Comput Methods Programs Biomed, 2020, 183: 105089.
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13.	Kuo C E, Chen G T, Lin N Y. Automatic sleep staging using deep long short-term memory: validation in large-scale datasets//3rd International Conference on Computational Biology and Bioinformatics (ICCBB), Nagoya: ACM, 2019: 58-64.
14.	Michielli N, Acharya U R, Molinari F. Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals. Comput Biol Med, 2019, 106: 71-81.
15.	Xu Z, Yang X, Sun J, et al. Sleep stage classification using time-frequency spectra from consecutive multi-time points. Front Neurosci, 2020, 14: 14.
16.	Yamabe M, Horie K, Shiokawa H, et al. MC-SleepNet: large-scale sleep stage scoring in mice by deep neural networks. Sci Rep, 2019, 9(1): 15793.
17.	Jeon Y, Kim S, Choi H S, et al. Pediatric sleep stage classification using multi-domain hybrid neural networks. IEEE Access, 2019, 7: 96495-96505.
18.	Niroshana S M I, Zhu Xin, Chen Ying, et al. Sleep stage classification based on EEG, EOG, and CNN-GRU deep learning model//2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST), Morioka: IEEE, 2019. DOI: 10.1109/ICAwST.2019.8923359.
19.	Chen K, Zhang C, Ma J, et al. Sleep staging from single-channel EEG with multi-scale feature and contextual information. Sleep Breath, 2019, 23(4): 1159-1167.
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21.	Phan H, Andreotti F, Cooray N, et al. SeqSleepNet: end-to-end hierarchical recurrent neural network for sequence-to-sequence automatic sleep staging. IEEE Trans Neural Syst Rehabil Eng, 2019, 27(3): 400-410.
22.	Yuan Y, Jia K, Ma F, et al. A hybrid self-attention deep learning framework for multivariate sleep stage classification. BMC Bioinformatics, 2019, 20(16): 586.
23.	Kemp B, Zwinderman A H, Tuk B, et al. Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE Trans Biomed Eng, 2000, 47(9): 1185-1194.
24.	Goldberger A L, Amaral L A, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 2000, 101(23): E215-E220.

1. Phan H, Andreotti F, Cooray N, et al. Joint classification and prediction CNN framework for automatic sleep stage classification. IEEE Trans Biomed Eng, 2019, 66(5): 1285-1296.
2. Iber C, Ancoli-Israel S, Chesson A L, et al. The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specifications. Westchester, IL: American academy of sleep medicine, 2007.
3. Zhang J, Yao R, Ge W, et al. Orthogonal convolutional neural networks for automatic sleep stage classification based on single-channel EEG. Comput Methods Programs Biomed, 2020, 183: 105089.
4. Liu G R, Lo Y L, Malik J, et al. Diffuse to fuse EEG spectra-intrinsic geometry of sleep dynamics for classification. Biomed Signal Process Control, 2020, 55: 101576.
5. Gopan K G, Prabhu S S, Sinha N. Sleep EEG analysis utilizing inter-channel covariance matrices. Biocybern Biomed Eng, 2020, 40(1): 527-545.
6. Huang W, Guo B, Shen Y, et al. Sleep staging algorithm based on multichannel data adding and multifeature screening. Comput Methods Programs Biomed, 2020, 187: 105253.
7. Yildirim O, Baloglu U B, Acharya U R. A deep learning model for automated sleep stages classification using PSG signals. Int J Environ Res Public Health, 2019, 16(4): 599.
8. Kanwal S, Uzair M, Ullah H, et al. An image based prediction model for sleep stage identification//2019 IEEE International Conference on Image Processing (ICIP), Taipei: IEEE, 2019: 1366-1370.
9. Wang J, Zhang Y, Ma Q, et al. Deep learning for single-channel EEG signals sleep stage scoring based on frequency domain representation//8th International Conference on Health Information Science (HIS), Xi'an: Springer, Cham, 2019(11837): 121-133.
10. Perslev M, Jensen M H, Darkner S, et al. U-time: a fully convolutional network for time series segmentation applied to sleep staging//33rd Conference on Neural Information Processing Systems (NeurIPS), Vancouver: NIPS, 2019(32): 4415-4426.
11. Wang Yutong, Wang Yikun, Yao Li, et al. Single Channel sleep staging based on unsupervised feature learning//2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP), Marrakesh: IEEE, 2019: 180-183.
12. Wei Yuhui, Qi Xia, Wang H, et al. A multi-class automatic sleep staging method based on long short-term memory network using single-lead electrocardiogram signals. IEEE Access, 2019, 7: 85959-85970.
13. Kuo C E, Chen G T, Lin N Y. Automatic sleep staging using deep long short-term memory: validation in large-scale datasets//3rd International Conference on Computational Biology and Bioinformatics (ICCBB), Nagoya: ACM, 2019: 58-64.
14. Michielli N, Acharya U R, Molinari F. Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals. Comput Biol Med, 2019, 106: 71-81.
15. Xu Z, Yang X, Sun J, et al. Sleep stage classification using time-frequency spectra from consecutive multi-time points. Front Neurosci, 2020, 14: 14.
16. Yamabe M, Horie K, Shiokawa H, et al. MC-SleepNet: large-scale sleep stage scoring in mice by deep neural networks. Sci Rep, 2019, 9(1): 15793.
17. Jeon Y, Kim S, Choi H S, et al. Pediatric sleep stage classification using multi-domain hybrid neural networks. IEEE Access, 2019, 7: 96495-96505.
18. Niroshana S M I, Zhu Xin, Chen Ying, et al. Sleep stage classification based on EEG, EOG, and CNN-GRU deep learning model//2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST), Morioka: IEEE, 2019. DOI: 10.1109/ICAwST.2019.8923359.
19. Chen K, Zhang C, Ma J, et al. Sleep staging from single-channel EEG with multi-scale feature and contextual information. Sleep Breath, 2019, 23(4): 1159-1167.
20. Sun C, Chen C, Li W, et al. A hierarchical neural network for sleep stage classification based on comprehensive feature learning and multi-flow sequence learning. IEEE J Biomed Health Inform, 2020, 24(5): 1351-1366.
21. Phan H, Andreotti F, Cooray N, et al. SeqSleepNet: end-to-end hierarchical recurrent neural network for sequence-to-sequence automatic sleep staging. IEEE Trans Neural Syst Rehabil Eng, 2019, 27(3): 400-410.
22. Yuan Y, Jia K, Ma F, et al. A hybrid self-attention deep learning framework for multivariate sleep stage classification. BMC Bioinformatics, 2019, 20(16): 586.
23. Kemp B, Zwinderman A H, Tuk B, et al. Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE Trans Biomed Eng, 2000, 47(9): 1185-1194.
24. Goldberger A L, Amaral L A, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 2000, 101(23): E215-E220.

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