Single-channel electroencephalogram signal used for sleep state recognition based on one-dimensional width kernel convolutional neural networks and long-short-term memory networks_Journal of Biomedical Engineering

Authors：

LIANG Jin ^1,3 ,  ZHOU Qiang ^1,3 , LI Wan ^2,3

1. School of Electrical and Control Engineering, Shaanxi University of Science & Technology, Xi’an 710021, P. R. China;
2. School of Electronic Information and Artificial Intelligence, Shaanxi University of Science & Technology, Xi’an 710021, P. R. China;
3. Shaanxi Artificial Intelligence Joint Laboratory, Xi'an 710021, P. R. China;

Corresponding author：

ZHOU Qiang, Email: zhouqiang@sust.edu.cn

Keywords：

Single-channel electroencephalogram signals; Sleep state recognition; Class imbalance; Convolutional neural networks; Long-short-term memory networks

DOI：

10.7507/1001-5515.202204021

Video：

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Aiming at the problem that the unbalanced distribution of data in sleep electroencephalogram(EEG) signals and poor comfort in the process of polysomnography information collection will reduce the model's classification ability, this paper proposed a sleep state recognition method using single-channel EEG signals (WKCNN-LSTM) based on one-dimensional width kernel convolutional neural networks(WKCNN) and long-short-term memory networks (LSTM). Firstly, the wavelet denoising and synthetic minority over-sampling technique-Tomek link (SMOTE-Tomek) algorithm were used to preprocess the original sleep EEG signals. Secondly, one-dimensional sleep EEG signals were used as the input of the model, and WKCNN was used to extract frequency-domain features and suppress high-frequency noise. Then, the LSTM layer was used to learn the time-domain features. Finally, normalized exponential function was used on the full connection layer to realize sleep state. The experimental results showed that the classification accuracy of the one-dimensional WKCNN-LSTM model was 91.80% in this paper, which was better than that of similar studies in recent years, and the model had good generalization ability. This study improved classification accuracy of single-channel sleep EEG signals that can be easily utilized in portable sleep monitoring devices.

Citation： LIANG Jin, ZHOU Qiang, LI Wan. Single-channel electroencephalogram signal used for sleep state recognition based on one-dimensional width kernel convolutional neural networks and long-short-term memory networks. Journal of Biomedical Engineering, 2022, 39(6): 1089-1096. doi: 10.7507/1001-5515.202204021 Copy

1.	Sharma M, Patel V, Acharya U R. Automated identification of insomnia using optimal bi-orthogonal wavelet transform technique with single-channel EEG signals. Knowledge-Based Systems, 2021, 224: 107078-107083.
2.	Dehnavi F, Moghimi S, Sadrabadi Haghighi S, et al. Opposite effect of motivated forgetting on sleep spindles during stage 2 and slow wave sleep. Sleep, 2019, 42(7): zsz085.
3.	Shi Min, Yang Chengyi, Zhang Dalu, et al. A smart detection method of sleep quality using EEG signal and long short-term memory model. Mathematical Problems in Engineering, 2021, 2021: 5515100.
4.	金峥, 贾克斌, 袁野. 基于混合注意力时序网络的睡眠分期算法研究. 生物医学工程学杂志, 2021, 38(2): 241-248.
5.	Rechtschaffen A Q, Kales A A. A manual of standardized terminology techniques and scoring system for sleep stages of human subjects. Psychiatry and Clinical Neurosciences, 1968, 55: 356-361.
6.	Ghimatgar H, Kazemi K, Helfroush M S, et al. An automatic single-channel EEG-based sleep stage scoring method based on hidden Markov model. Journal of Neuroscience Methods, 2019, 324: 108320.
7.	Gupta V, Pachori R B. FBDM based time-frequency representation for sleep stages classification using EEG signals. Biomedical Signal Processing and Control, 2021, 64: 2265-2281.
8.	Yan Rui, Zhang Chi, Spruyt K, et al. Multi-modality of polysomnography signals’ fusion for automatic sleep scoring. Biomedical Signal Processing and Control, 2019, 49: 14-23.
9.	Kuo C E, Chen G T. Automatic sleep staging based on a hybrid stacked LSTM neural network: verification using large-scale dataset. IEEE Access, 2020, 8: 11837-11849.
10.	Cui Jian, Lan Zirui, Liu Yisi, et al. A compact and interpretable convolutional neural network for cross-subject driver drowsiness detection from single-channel EEG. Methods, 2022, 202: 173-184.
11.	Phanikrishna Venkata B, Chinara S. Automatic classification methods for detecting drowsiness using wavelet packet transform extracted time-domain features from single-channel EEG signal. Journal of Neuroscience Methods, 2020, 347: 108927.
12.	Fan L A, Rui Y, Rma B, et al. End-to-end sleep staging using convolutional neural network in raw single-channel EEG. Biomedical Signal Processing and Control, 2021, 63: 102203-102210.
13.	Supratak A, Dong H, Wu C, et al. DeepSleepNet: a model for automatic sleep stage scoring based on raw single-channel EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017, 25(11): 1998-2008.
14.	Huang Y, Zhang Y, Yan C. Automatic sleep staging based on deep neural network using single channel EEG∥International Conference on Knowledge Management in Organizations, Zamora: KMO, 2019: 63-73.
15.	Seifpour S, Niknazar H, Mikaeili M, et al. A new automatic sleep staging system based on statistical behavior of local extrema using single channel EEG signal. Expert Systems with Application, 2018, 104: 277-293.
16.	Jiang D, Lu Y N, Yu M, et al. Robust sleep stage classification with single-channel EEG signals using multimodal decomposition and HMM-based refinement. Expert Systems with Applications, 2019, 121: 188-203.
17.	Hassan A R, Bhuiyan M. An automated method for sleep staging from EEG signals using normal inverse Gaussian parameters and adaptive boosting. Neurocomputing, 2016, 219(5): 76-87.
18.	Wuzheng Xiaolei, Zuo Shigang, Yao Li, et al. Semi-supervised sparse representation classification for sleep EEG recognition with imbalanced sample sets. Journal of Mechanics in Medicine and Biology, 2021, 21(5): 2140006.
19.	Kingma D, Ba J. Adam: a method for stochastic optimization. International Conference on Learning Representations, 2014, 12: 506-514.
20.	Kemp B, Zwinderman A H, Tuk B, et al. Analysis of asleep-dependent neuronal feedback loop:the slow-wave microcontinuity of the EEG. IEEE Trans Biomed Eng, 2000, 47(9): 1185-1194.
21.	Wolpert E A. A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects. Electroencephalography & Clinical Neurophysiology, 1969, 26(2): 644-644.
22.	Chen X, He J, Wu X, et al. Sleep staging by bidirectional long short-term memory convolution neural network. Future Generation Computer Systems, 2020, 109(6): 188-196.
23.	Phan H, Andreotti F, Cooray N, et a1. SeqSleepNet: end-to-end hierarchical recurrent neural network for sequence-to-sequence automatic sleep staging. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019, 27(3): 400-410.
24.	Seo H, Back S, Lee S, et al. Intra-and inter-epoch temporal context network (IITNet) using sub-epoch features for automatic sleep scoring on raw single-channel EEG. Biomedical Signal Processing and Control, 2020, 61: 2037-2048.
25.	Zhang J, Wu Y. Competition convolutional neural network for sleep stage classification. Biomedical Signal Processing and Control, 2021, 64: 102318-102327.
26.	Eldele E, Chen Z, Liu C, et al. An attention-based deep learning approach for sleep stage classification with single-channel EEG. IEEE Trans Neural Syst Rehabil Eng, 2021, 29: 809-818.
27.	Khalili E, Mohammadzadeh Asl B. Automatic sleep stage classification using temporal convolutional neural network and new data augmentation technique from raw single-channel EEG. Comput Methods Programs Biomed, 2021, 204: 106063.

1. Sharma M, Patel V, Acharya U R. Automated identification of insomnia using optimal bi-orthogonal wavelet transform technique with single-channel EEG signals. Knowledge-Based Systems, 2021, 224: 107078-107083.
2. Dehnavi F, Moghimi S, Sadrabadi Haghighi S, et al. Opposite effect of motivated forgetting on sleep spindles during stage 2 and slow wave sleep. Sleep, 2019, 42(7): zsz085.
3. Shi Min, Yang Chengyi, Zhang Dalu, et al. A smart detection method of sleep quality using EEG signal and long short-term memory model. Mathematical Problems in Engineering, 2021, 2021: 5515100.
4. 金峥, 贾克斌, 袁野. 基于混合注意力时序网络的睡眠分期算法研究. 生物医学工程学杂志, 2021, 38(2): 241-248.
5. Rechtschaffen A Q, Kales A A. A manual of standardized terminology techniques and scoring system for sleep stages of human subjects. Psychiatry and Clinical Neurosciences, 1968, 55: 356-361.
6. Ghimatgar H, Kazemi K, Helfroush M S, et al. An automatic single-channel EEG-based sleep stage scoring method based on hidden Markov model. Journal of Neuroscience Methods, 2019, 324: 108320.
7. Gupta V, Pachori R B. FBDM based time-frequency representation for sleep stages classification using EEG signals. Biomedical Signal Processing and Control, 2021, 64: 2265-2281.
8. Yan Rui, Zhang Chi, Spruyt K, et al. Multi-modality of polysomnography signals’ fusion for automatic sleep scoring. Biomedical Signal Processing and Control, 2019, 49: 14-23.
9. Kuo C E, Chen G T. Automatic sleep staging based on a hybrid stacked LSTM neural network: verification using large-scale dataset. IEEE Access, 2020, 8: 11837-11849.
10. Cui Jian, Lan Zirui, Liu Yisi, et al. A compact and interpretable convolutional neural network for cross-subject driver drowsiness detection from single-channel EEG. Methods, 2022, 202: 173-184.
11. Phanikrishna Venkata B, Chinara S. Automatic classification methods for detecting drowsiness using wavelet packet transform extracted time-domain features from single-channel EEG signal. Journal of Neuroscience Methods, 2020, 347: 108927.
12. Fan L A, Rui Y, Rma B, et al. End-to-end sleep staging using convolutional neural network in raw single-channel EEG. Biomedical Signal Processing and Control, 2021, 63: 102203-102210.
13. Supratak A, Dong H, Wu C, et al. DeepSleepNet: a model for automatic sleep stage scoring based on raw single-channel EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017, 25(11): 1998-2008.
14. Huang Y, Zhang Y, Yan C. Automatic sleep staging based on deep neural network using single channel EEG∥International Conference on Knowledge Management in Organizations, Zamora: KMO, 2019: 63-73.
15. Seifpour S, Niknazar H, Mikaeili M, et al. A new automatic sleep staging system based on statistical behavior of local extrema using single channel EEG signal. Expert Systems with Application, 2018, 104: 277-293.
16. Jiang D, Lu Y N, Yu M, et al. Robust sleep stage classification with single-channel EEG signals using multimodal decomposition and HMM-based refinement. Expert Systems with Applications, 2019, 121: 188-203.
17. Hassan A R, Bhuiyan M. An automated method for sleep staging from EEG signals using normal inverse Gaussian parameters and adaptive boosting. Neurocomputing, 2016, 219(5): 76-87.
18. Wuzheng Xiaolei, Zuo Shigang, Yao Li, et al. Semi-supervised sparse representation classification for sleep EEG recognition with imbalanced sample sets. Journal of Mechanics in Medicine and Biology, 2021, 21(5): 2140006.
19. Kingma D, Ba J. Adam: a method for stochastic optimization. International Conference on Learning Representations, 2014, 12: 506-514.
20. Kemp B, Zwinderman A H, Tuk B, et al. Analysis of asleep-dependent neuronal feedback loop:the slow-wave microcontinuity of the EEG. IEEE Trans Biomed Eng, 2000, 47(9): 1185-1194.
21. Wolpert E A. A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects. Electroencephalography & Clinical Neurophysiology, 1969, 26(2): 644-644.
22. Chen X, He J, Wu X, et al. Sleep staging by bidirectional long short-term memory convolution neural network. Future Generation Computer Systems, 2020, 109(6): 188-196.
23. Phan H, Andreotti F, Cooray N, et a1. SeqSleepNet: end-to-end hierarchical recurrent neural network for sequence-to-sequence automatic sleep staging. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019, 27(3): 400-410.
24. Seo H, Back S, Lee S, et al. Intra-and inter-epoch temporal context network (IITNet) using sub-epoch features for automatic sleep scoring on raw single-channel EEG. Biomedical Signal Processing and Control, 2020, 61: 2037-2048.
25. Zhang J, Wu Y. Competition convolutional neural network for sleep stage classification. Biomedical Signal Processing and Control, 2021, 64: 102318-102327.
26. Eldele E, Chen Z, Liu C, et al. An attention-based deep learning approach for sleep stage classification with single-channel EEG. IEEE Trans Neural Syst Rehabil Eng, 2021, 29: 809-818.
27. Khalili E, Mohammadzadeh Asl B. Automatic sleep stage classification using temporal convolutional neural network and new data augmentation technique from raw single-channel EEG. Comput Methods Programs Biomed, 2021, 204: 106063.

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Single-channel electroencephalogram signal used for sleep state recognition based on one-dimensional width kernel convolutional neural networks and long-short-term memory networks

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