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.
|