Automatic sleep staging model based on single channel electroencephalogram signal_Journal of Biomedical Engineering

Authors：

ZHANG Haowei ¹ , XU Zhe ¹ , YUAN Chengmei ² , JI Caojun ² ,  LIU Ying ¹

1. School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China;
2. Shanghai Mental Health Center, Shanghai 200030, P. R. China;

Corresponding author：

LIU Ying, Email: ling2431@163.com

Keywords：

Automatic sleep staging; Single-channel electroencephalogram signal; Convolutional neural network; Bi-directional long short-term memory network

DOI：

10.7507/1001-5515.202210072

Video：

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Abstract Full text Figures/Tables Video References Cited by

Sleep staging is the basis for solving sleep problems. There’s an upper limit for the classification accuracy of sleep staging models based on single-channel electroencephalogram (EEG) data and features. To address this problem, this paper proposed an automatic sleep staging model that mixes deep convolutional neural network (DCNN) and bi-directional long short-term memory network (BiLSTM). The model used DCNN to automatically learn the time-frequency domain features of EEG signals, and used BiLSTM to extract the temporal features between the data, fully exploiting the feature information contained in the data to improve the accuracy of automatic sleep staging. At the same time, noise reduction techniques and adaptive synthetic sampling were used to reduce the impact of signal noise and unbalanced data sets on model performance. In this paper, experiments were conducted using the Sleep-European Data Format Database Expanded and the Shanghai Mental Health Center Sleep Database, and achieved an overall accuracy rate of 86.9% and 88.9% respectively. When compared with the basic network model, all the experimental results outperformed the basic network, further demonstrating the validity of this paper's model, which can provide a reference for the construction of a home sleep monitoring system based on single-channel EEG signals.

Citation： ZHANG Haowei, XU Zhe, YUAN Chengmei, JI Caojun, LIU Ying. Automatic sleep staging model based on single channel electroencephalogram signal. Journal of Biomedical Engineering, 2023, 40(3): 458-464. doi: 10.7507/1001-5515.202210072 Copy

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18.	Alhudhaif A. A novel multi-class imbalanced EEG signals classification based on the adaptive synthetic sampling (ADASYN) approach. PeerJ Comput Sci, 2021, 7: e523.
19.	Datta D, Mallick P K, Reddy A V N, et al. A hybrid classification of imbalanced hyperspectral images using ADASYN and enhanced deep subsampled multi-grained cascaded forest. Remote Sens, 2022, 14(19): 4853.
20.	Roy A M. An efficient multi-scale CNN model with intrinsic feature integration for motor imagery EEG subject classification in brain-machine interfaces. Biomedical Signal Processing and Control, 2022, 74: 103496.
21.	沈雷, 耿馨佚, 王守. 基于迁移学习和空洞卷积的癫痫状态识别方法[J]. 中国生物医学工程学报, 2020, 39(6): 700-710.
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23.	Ji X, Li Y, Wen P. Jumping knowledge based spatial-temporal graph convolutional networks for automatic sleep stage classification. IEEE Trans Neural Syst Rehabil Eng, 2022, 30: 1464-1472.

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. Rundo J V, Downey R. Polysomnography. Handb Clin Neurol, 2019, 160: 381-392.
3. Berry R B, Budhiraja R, Gottlieb D J, et al. Rules for scoring respiratory events in sleep: update of the 2007 AASM manual for the scoring of sleep and associated events. Deliberations of the sleep apnea definitions task force of the American Academy of Sleep Medicine. J Clin Sleep Med, 2012, 8(5): 597-619.
4. Li Q, Li Q, Liu C, et al. Deep learning in the cross-time frequency domain for sleep staging from a single-lead electrocardiogram. Physiol Meas, 2018, 39(12): 124005.
5. Zhu T, Luo W, Yu F. Convolution-and attention-based neural network for automated sleep stage classification. Int J Environ Res Public Health, 2020, 17(11): 4152.
6. Zhang X, Xu M, Li Y, et al. Automated multi-model deep neural network for sleep stage scoring with unfiltered clinical data[J]. Sleep Breath, 2020, 24(2): 581-590.
7. Cui Zhihong, Zheng Xiangwei, Shao Xuexiao, et al. Automatic sleep stage classification based on convolutional neural network and fine-grained segments. Complexity, 2018, 2018: 9248410.
8. Hsu Y L, Yang Y T, Wang J S, et al. Automatic sleep stage recurrent neural classifier using energy features of EEG signals. Neurocomputing, 2013, 104: 105-114.
9. 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.
10. You Y, Zhong X, Liu G, et al. Automatic sleep stage classification: a light and efficient deep neural network model based on time, frequency and fractional Fourier transform domain features. Artif Intell Med, 2022, 127: 102279.
11. Fu M, Wang Y, Chen Z, et al. Deep learning in automatic sleep staging with a single channel electroencephalography. Front Psychol, 2021, 12: 628502.
12. Zhuang L, Dai M, Zhou Y, et al. Intelligent automatic sleep staging model based on CNN and LSTM. Front Public Health, 2022, 10: 946833.
13. Zhou W, Zhu H, Shen N, et al. A lightweight segmented attention network for sleep staging by fusing local characteristics and adjacent information. IEEE Trans Neural Syst Rehabil Eng, 2023, 31: 238-247.
14. Chang R B. A journey toward artificial intelligence-assisted automated sleep scoring. Patterns, 2022, 3(1): 100429.
15. Casciola A A, Carlucci S K, Kent B A, et al. A deep learning strategy for automatic sleep staging based on two-channel EEG headband data. Sensors, 2021, 21(10): 3316.
16. Özer İ, Efe S B, Özbay H. CNN/Bi-LSTM-based deep learning algorithm for classification of power quality disturbances by using spectrogram images. International Transactions on Electrical Energy Systems, 2021. DOI: 10.1002/2050-7038.13204.
17. Bairagi R N, Maniruzzaman M, Pervin S, et al. Epileptic seizure identification in EEG signals using DWT, ANN and sequential window algorithm. Soft Computing Letters, 2021, 3: 100026.
18. Alhudhaif A. A novel multi-class imbalanced EEG signals classification based on the adaptive synthetic sampling (ADASYN) approach. PeerJ Comput Sci, 2021, 7: e523.
19. Datta D, Mallick P K, Reddy A V N, et al. A hybrid classification of imbalanced hyperspectral images using ADASYN and enhanced deep subsampled multi-grained cascaded forest. Remote Sens, 2022, 14(19): 4853.
20. Roy A M. An efficient multi-scale CNN model with intrinsic feature integration for motor imagery EEG subject classification in brain-machine interfaces. Biomedical Signal Processing and Control, 2022, 74: 103496.
21. 沈雷, 耿馨佚, 王守. 基于迁移学习和空洞卷积的癫痫状态识别方法[J]. 中国生物医学工程学报, 2020, 39(6): 700-710.
22. 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.
23. Ji X, Li Y, Wen P. Jumping knowledge based spatial-temporal graph convolutional networks for automatic sleep stage classification. IEEE Trans Neural Syst Rehabil Eng, 2022, 30: 1464-1472.

Journal of Biomedical Engineering

Automatic sleep staging model based on single channel electroencephalogram signal

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