Study on the method of polysomnography sleep stage staging based on attention mechanism and bidirectional gate recurrent unit_Journal of Biomedical Engineering

Authors：

LIU Ying ¹ , HE Changle ¹ , YUAN Chengmei ² ,  ZHANG Haowei ¹ ,  JI Caojun ²

1. School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China;
2. Sleep disorder ward of clinical psychology department, Shanghai Mental Health Center, Shanghai 200030, P. R. China;

Corresponding author：

ZHANG Haowei, Email: howiezh@aliyun.com; JI Caojun, Email: jicaojun1985@163.com

Keywords：

Polysomnography; Sleep stage; Self-attention mechanism; Bidirectional gate recurrent unit

DOI：

10.7507/1001-5515.202208017

Video：

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Abstract Full text Figures/Tables Video References Cited by

Polysomnography (PSG) monitoring is an important method for clinical diagnosis of diseases such as insomnia, apnea and so on. In order to solve the problem of time-consuming and energy-consuming sleep stage staging of sleep disorder patients using manual frame-by-frame visual judgment PSG, this study proposed a deep learning algorithm model combining convolutional neural networks (CNN) and bidirectional gate recurrent neural networks (Bi GRU). A dynamic sparse self-attention mechanism was designed to solve the problem that gated recurrent neural networks (GRU) is difficult to obtain accurate vector representation of long-distance information. This study collected 143 overnight PSG data of patients from Shanghai Mental Health Center with sleep disorders, which were combined with 153 overnight PSG data of patients from the open-source dataset, and selected 9 electrophysiological channel signals including 6 electroencephalogram (EEG) signal channels, 2 electrooculogram (EOG) signal channels and a single mandibular electromyogram (EMG) signal channel. These data were used for model training, testing and evaluation. After cross validation, the accuracy was (84.0±2.0)%, and Cohen's kappa value was 0.77±0.50. It showed better performance than the Cohen's kappa value of physician score of 0.75±0.11. The experimental results show that the algorithm model in this paper has a high staging effect in different populations and is widely applicable. It is of great significance to assist clinicians in rapid and large-scale PSG sleep automatic staging.

Citation： LIU Ying, HE Changle, YUAN Chengmei, ZHANG Haowei, JI Caojun. Study on the method of polysomnography sleep stage staging based on attention mechanism and bidirectional gate recurrent unit. Journal of Biomedical Engineering, 2023, 40(1): 35-43. doi: 10.7507/1001-5515.202208017 Copy

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17.	Shahid F, Zameer A, Muneeb M. Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM. Chaos Solitons Fractals, 2020, 140: 110212.
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20.	梁斌, 刘全, 徐进, 等. 基于多注意力卷积神经网络的特定目标情感分析. 计算机研究与发展, 2017, 54(08): 1724-1735.
21.	张兰霞, 胡文心. 基于双向GRU神经网络和双层注意力机制的中文文本中人物关系抽取研究. 计算机应用与软件, 2018, 35(11): 130-135, 189.
22.	Wang S H, Muhammad K, Hong J, et al. Alcoholism identification via convolutional neural network based on parametric ReLU, dropout, and batch normalization. Neural Computing & Applications, 2020, 32(3): 665-680.
23.	Chang Z, Zhang Y, Chen W. Electricity price prediction based on hybrid model of adam optimized LSTM neural network and wavelet transform. Energy, 2019, 187: 115804.
24.	Chambon S, Galtier M N, Arnal P J, et al. A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018, 26(4): 758-769.
25.	Zaharchuk G, Gong E, Wintermark M, et al. Deep learning in neuroradiology. AJNR Am J Neuroradiol, 2018, 39(10): 1776-1784.
26.	Deng S J, Zhang X, Zhang Y, et al. Interrater agreement between American and Chinese sleep centers according to the 2014 AASM standard. Sleep and Breathing, 2019, 23(2): 719-728.

1. Rechtschaffen A, Kales A. A manual of standardized terminology, techniques and scoring system for sleep stages in human subjects. Los Angeles: UCLA Brain Information Service/Brain Research Institute, 1968.
2. Berry R B, Brooks R, Gamaldo C, et al. AASM scoring manual updates for 2017 (version 2. 4) . J Clin Sleep Med, 2017, 13(5): 665-666.
3. Stepnowsky C, Levendowski D, Popovic D, et al. Scoring accuracy of automated sleep staging from a bipolar electroocular recording compared to manual scoring by multiple raters. Sleep Medicine, 2013, 14(11): 1199-1207.
4. Şen B, Peker M, Çavuşoğlu A, et al. A comparative study on classification of sleep stage based on EEG signals using feature selection and classification algorithms. Journal of medical systems, 2014, 38(3): 18.
5. Cervantes J, Garcia-Lamont F, Rodríguez-Mazahua L, et al. A comprehensive survey on support vector machine classification: applications, challenges and trends. Neurocomputing, 2020, 408: 189-215.
6. Lee C S, Cheang P Y S, Moslehpour M. Predictive analytics in business analytics: decision tree. Advances in Decision Sciences, 2022, 26(1): 1-29.
7. Mei S H, Ji J Y, Hou J H, et al. Learning sensor-specific spatial-spectral features of hyperspectral images via convolutional neural networks. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(8): 4520-4533.
8. Yu Y, Si X, Hu C, et al. A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput, 2019, 31(7): 1235-1270.
9. Längkvist M, Karlsson L, Loutfi A. Sleep stage classification using unsupervised feature learning. Advances in Artificial Neural Systems, 2012: 107046.
10. Supratak A, Dong H, Wu C, et al. DeepSleepNet: A model for automatic sleep stage scoring based on raw single-channel EEG. IEEE Trans Neural Syst Rehabil Eng, 2017, 25(11): 1998-2008.
11. Zhang L D, Fabbri D, Upender R, et al. Automated sleep stage scoring of the sleep heart health study using deep neural networks. Sleep, 2019, 42(11): zsz159.
12. Gao S, Huang Y F, Zhang S, et al. Short-term runoff prediction with GRU and LSTM networks without requiring time step optimization during sample generation. Journal of Hydrology, 2020, 589: 125188.
13. Alvarez-Estevez D, Rijsman R M. Inter-database validation of a deep learning approach for automatic sleep scoring. PLoS One, 2021, 16(8): e0256111.
14. Mousavi S M, Langston C A, Horton S P. Automatic microseismic denoising and onset detection using the synchrosqueezed continuous wavelet transform. Geophysics, 2016, 81(4): V341-V355.
15. Yildirim O, Baloglu U B, Acharya U R. A deep learning model for automated sleep stages classification using PSG signals. International Journal of Environmental Research and Public Health, 2019, 16(4): 599.
16. Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput, 1997, 9(8): 1735-1780.
17. Shahid F, Zameer A, Muneeb M. Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM. Chaos Solitons Fractals, 2020, 140: 110212.
18. Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017: 6000-6010.
19. Kuo C E, Liao P Y, Lin Y S. A Self-attention-based ensemble convolution neural network approach for sleep stage classification with merged spectrogram//2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2021: 1262-1268.
20. 梁斌, 刘全, 徐进, 等. 基于多注意力卷积神经网络的特定目标情感分析. 计算机研究与发展, 2017, 54(08): 1724-1735.
21. 张兰霞, 胡文心. 基于双向GRU神经网络和双层注意力机制的中文文本中人物关系抽取研究. 计算机应用与软件, 2018, 35(11): 130-135, 189.
22. Wang S H, Muhammad K, Hong J, et al. Alcoholism identification via convolutional neural network based on parametric ReLU, dropout, and batch normalization. Neural Computing & Applications, 2020, 32(3): 665-680.
23. Chang Z, Zhang Y, Chen W. Electricity price prediction based on hybrid model of adam optimized LSTM neural network and wavelet transform. Energy, 2019, 187: 115804.
24. Chambon S, Galtier M N, Arnal P J, et al. A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018, 26(4): 758-769.
25. Zaharchuk G, Gong E, Wintermark M, et al. Deep learning in neuroradiology. AJNR Am J Neuroradiol, 2018, 39(10): 1776-1784.
26. Deng S J, Zhang X, Zhang Y, et al. Interrater agreement between American and Chinese sleep centers according to the 2014 AASM standard. Sleep and Breathing, 2019, 23(2): 719-728.

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