基于深度学习的脑电信号自动睡眠分期研究进展
Research Progress of EEG Automatic Sleep Staging Based on Deep Learning
DOI: 10.12677/AAM.2023.121004, PDF,    科研立项经费支持
作者: 许 哲, 章浩伟*, 刘 颖：上海理工大学健康科学与工程学院，上海
关键词: 睡眠；脑电信号；深度学习；自动分期；Sleep； Eeg Signal； Deep Learning； Automatic Staging
摘要: 睡眠是人类不可或缺的生理活动，准确地睡眠分期是诊断睡眠疾病的前提。当前，基于深度学习的脑电信号自动睡眠分期正成为研究的热点，虽然相关研究取得很多进展，但距离临床应用还有一定距离。本文就该领域展开综述，详细介绍了近年来基于深度学习的脑电信号自动睡眠分期方法，综合论述目前主流神经网络在自动睡眠分期领域的研究现状及进展，分析归纳了不同模型网络的潜力优势及未来发展方向，以促进深度学习技术在基于脑电信号的自动分期研究更深入发展。
Abstract: Sleep is an indispensable physiological activity of human beings. Accurate sleep staging is the premise of diagnosing sleep diseases. At present, EEG automatic sleep staging based on deep learn-ing is becoming a hot research topic. Although related researches have made a lot of progress, there is still a long way to go before clinical application. This paper reviews this field, introduces in detail the EEG automatic sleep staging methods based on deep learning in recent years, comprehensively discusses the current research status and progress of mainstream neural networks in the field of automatic sleep staging, analyzes and summarizes the potential advantages and future develop-ment direction of different model networks. In order to promote the deep learning technology in the automatic staging based on EEG further development.
文章引用：许哲, 章浩伟, 刘颖. 基于深度学习的脑电信号自动睡眠分期研究进展[J]. 应用数学进展, 2023, 12(1): 21-28. https://doi.org/10.12677/AAM.2023.121004

参考文献

[1] Czeisler, C.A. (2015) Duration, Timing and Quality of Sleep Are Each Vital for Health, Performance and Safety. Sleep Health: Journal of the National Sleep Foundation, 1, 5-8. [Google Scholar] [CrossRef] [PubMed]
[2] Phan, H., Andreotti, F., Cooray, N., Chén, O.Y. and De Vos, M. (2018) Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification. IEEE Transactions on Biomedical Engineering, 66, 1285-1296. [Google Scholar] [CrossRef] 
[3] Phan, H., Mikkelsen, K., Chén, O.Y., et al. (2022) SleepTrans-former: Automatic Sleep Staging with Interpretability and Uncertainty Quantification. IEEE Transactions on Biomedical Engineering, 69, 2456-2467. [Google Scholar] [CrossRef] 
[4] Phan, H. and Mikkelsen, K. (2022) Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions. Physiological Measurement, 43, Article ID: 04TR01. [Google Scholar] [CrossRef] [PubMed]
[5] Mousavi, S., Afghah, F. and Acharya, U.R. (2019) SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach. PLOS ONE, 14, e0216456. [Google Scholar] [CrossRef] [PubMed]
[6] Clements-Cortes, A and Kim, C.T. (2017) Music for a Better Night’s Sleep. Canadian Music Educator, 58, 33-36.
[7] Ma, N., Wu, Z., Cheung, Y. M., Guo, Y., Gao, Y., Li, J. and Jiang, B. (2022) A Survey of Human Action Recognition and Posture Prediction. Tsinghua Science and Tech-nology, 27, 973-1001. [Google Scholar] [CrossRef] 
[8] 朱方圆, 马志强, 陈艳, 张晓旭, 王洪彬, 宝财吉拉呼. 语音识别中说话人自适应方法研究综述[J]. 计算机科学与探索, 2021, 15(12): 2241-2255.
[9] Shankar, V. and Parsa-na, S. (2022) An Overview and Empirical Comparison of Natural Language Processing (NLP) Models and an Introduc-tion to and Empirical Application of Autoencoder Models in Marketing. Journal of the Academy of Marketing Science, 50, 1324-1350. [Google Scholar] [CrossRef] 
[10] Kamath, S., Karibasappa, K.G., Reddy, A., Kallur, A.M., Priyanka, B.B. and Bhagya, B.P. (2021) Improving the Relation Classification Using Convolutional Neural Net-work. IOP Conference Series: Materials Science and Engineering, 1187, Article ID: 012004. [Google Scholar] [CrossRef] 
[11] Li, Q., Chen, Y. and Zeng, Y. (2022) Transformer with Transfer CNN for Remote-Sensing-Image Object Detection. Remote Sensing, 14, Article No. 984. [Google Scholar] [CrossRef] 
[12] Ek, A. and Bma, A. (2021) Automatic Sleep Stage Classification Using Temporal Convolutional Neural Network and New Data Augmentation Technique from Raw Single-Channel EEG. Computer Methods and Programs in Biomedicine, 204, Article ID: 106063.
[13] Phan, H., Andreotti, F., Cooray, N., Chén, O.Y. and De Vos, M. (2018) Automatic Sleep Stage Classification Using Single-Channel EEG: Learning Sequen-tial Features with Attention-Based Recurrent Neural Networks. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Honolulu, 18-21 July 2018, 1452-1455. [Google Scholar] [CrossRef] 
[14] Casson, A.J., Yates, D.C., Smith, S.J.M., et al. (2010) Weara-ble Electroencephalography. IEEE Engineering in Medicine and Biology Magazine, 29, 44-56. [Google Scholar] [CrossRef] 
[15] Jasper, H.H. (1958) The 10-20 Electrode System of the Interna-tional Federation. Electroencephalography and Clinical Neurophysiology, 10, 370-375. [Google Scholar] [CrossRef] 
[16] Wolpert, E.A. (1969) A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects. Electroencephalography & Clinical Neurophysi-ology, 26, 644-644. [Google Scholar] [CrossRef] 
[17] Iber, C., Ancoli-Israel, S., Chesson, A.L., et al. (2015) The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications. American Academy of Sleep Medicine, Westchester IL.
[18] Goldberger, A.L., Amaral, L.A., Glass, L., et al. (2000) PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation, 101, e215-e220. [Google Scholar] [CrossRef] 
[19] Kemp, B., Zwinderman, A.H., Tuk, B., et al. (2000) Analysis of a Sleep-Dependent Neuronal Feedback Loop: The Slow-Wave Microcontinuity of the EEG. IEEE Transactions on Bio-medical Engineering, 47, 1185-1194. [Google Scholar] [CrossRef] [PubMed]
[20] Zhang, G.-Q., Cui, L., Mueller, R., et al. (2018) The National Sleep Re-search Resource: Towards a Sleep Data Commons. Journal of the American Medical Informatics Association, 25, 1351-1358. [Google Scholar] [CrossRef] [PubMed]
[21] Ichimaru, Y and Moody, G.B. (1999) Development of the Polysomnographic Database on CD-ROM. Psychiatry and Clinical Neurosciences, 53, 175-177. [Google Scholar] [CrossRef] [PubMed]
[22] Khalighi, S., Sousa, T., Santos, J.M. and Nunes, U. (2016) ISRUC-Sleep: A Comprehensive Public Dataset for Sleep Researchers. Computer Methods and Programs in Biomedi-cine, 124, 180-192. [Google Scholar] [CrossRef] [PubMed]
[23] O’Reilly, C., Gosselin, N., Carrier, J. and Nielsen, T. (2014) Montreal Archive of Sleep Studies: An Open-Access Resource for Instrument Benchmarking and Exploratory Research. Journal of Sleep Research, 23, 628-635. [Google Scholar] [CrossRef] [PubMed]
[24] Ye, F., Xu, S., Wang, T., Wang, Z. and Ren, T. (2021) Application of CNN Algorithm Based on Chaotic Recursive Diagonal Model in Medical Image Processing. Computational Intelligence and Neuroscience, 2021, Article ID: 6168562. [Google Scholar] [CrossRef] [PubMed]
[25] Hu, X., Shi, W., Zhou, Y., Tang, H. and Duan, S. (2022) Quantized and Adaptive Memristor Based CNN (QA-mCNN) for Image Processing. Sci-ence China Information Sciences, 65, Article No. 119104. [Google Scholar] [CrossRef] 
[26] LeCun, Y. and Bengio, Y. (1995) Convolutional Networks for Images, Speech, and Time Series. In: Arbib, M.A., Ed., The Handbook of Brain Theory and Neural Networks, MIT Press, Cambridge, 255-258.
[27] Cecotti, H. and Graser, A. (2010) Convolutional Neural Networks for P300 Detec-tion with Application to Brain-Com- puter Interfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 233, 433-445. [Google Scholar] [CrossRef] 
[28] Tsinalis, O., Matthews, P.M., Guo, Y. and Zafeiriou, S. (2016) Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks. ArXiv Preprint ArXiv: 1610. 01683.
[29] Zhu, T., Luo, W. and Yu, F. (2020) Convolution- and Attention-Based Neural Network for Auto-mated Sleep Stage Classification. International Journal of Environmental Research and Public Health, 17, Article No. 4152. [Google Scholar] [CrossRef] [PubMed]
[30] Zhang, X., Xu, M., Li, Y., Su, M., et al. (2020) Automated Mul-ti-Model Deep Neural Network for Sleep Stage Scoring with Unfiltered Clinical Data. Sleep and Breathing, 24, 581-590. [Google Scholar] [CrossRef] [PubMed]
[31] Cui, Z., Zheng, X., Shao, X. and Cui, L. (2018) Automatic Sleep Stage Classification Based on Convolutional Neural Network and Fine-Grained Segments. Complexity, 2018, Arti-cle ID: 9248410. [Google Scholar] [CrossRef] 
[32] Michielli, N., Acharya, U.R. and Molinari, F. (2019) Cascaded LSTM Recurrent Neural Network for Automated Sleep Stage Classification Using Single-Channel EEG Sig-nals. Computers in Biology and Medicine, 106, 71-81. [Google Scholar] [CrossRef] [PubMed]
[33] Lee, J.H. and Hong, J.K. (2022) Comparative Performance Analysis of Vibration Prediction Using RNN Techniques. Electronics, 11, Article No. 3619. [Google Scholar] [CrossRef] 
[34] Samaan, G.H., Wadie, A.R., Attia, A.K., Asaad, A.M., Kamel, A.E., Slim, S.O., Abdallah, M.S. and Cho, Y.-I. (2022) MediaPipe’s Landmarks with RNN for Dynamic Sign Language Recognition. Electronics, 11, Article No. 3228. [Google Scholar] [CrossRef] 
[35] Zhu, X., Han, Y., Li, S. and Wang, X. (2022) A Spa-tial-Temporal Topic Model with Sparse Prior and RNN Prior for Bursty Topic Discovering in Social Networks. Journal of Intelligent & Fuzzy Systems, 42, 3909-3922. [Google Scholar] [CrossRef] 
[36] Hsu, Y.-L., Yang, Y.-T., Wang, J.-S. and Hsu, C.-Y. (2013) Automatic Sleep Stage Recurrent Neural Classifier Using Energy Features of EEG Signals. Neurocomputing, 104, 105-114. [Google Scholar] [CrossRef] 
[37] You, Y., Zhong, X., Liu, G. and Yang, Z. (2022) Automatic Sleep Stage Classification: A Light and Efficient Deep Neural Network Model Based on Time, Frequency and Fractional Fourier Transform Domain Features. Artificial Intelligence in Medicine, 127, Article ID: 102279. [Google Scholar] [CrossRef] [PubMed]
[38] Supratak, A., Dong, H., Wu, C. and Guo, Y. (2017) DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25, 1998-2008. [Google Scholar] [CrossRef] 
[39] Fu, M., Wang, Y., Chen, Z., et al. (2021) Deep Learning in Automatic Sleep Staging With a Single Channel Electroencephalography. Frontiers in Physiology, 12, Article 628502. [Google Scholar] [CrossRef] [PubMed]
[40] Zhuang, L., Dai, M., Zhou, Y. and Sun, L. (2011) Intelligent Au-tomatic Sleep Staging Model Based on CNN and LSTM. Frontiers in Public Health, 10, Article 946833. [Google Scholar] [CrossRef] [PubMed]
[41] Zhou, W., Zhu, H., Shen, N., et al. (2011) A Lightweight Seg-mented Attention Network for Sleep Staging by Fusing Local Characteristics and Adjacent Information. IEEE Transac-tions on Neural Systems and Rehabilitation Engineering. [Google Scholar] [CrossRef] 
[42] Chang, R.B. (2022) A Journey toward Artificial Intelli-gence-Assisted Automated Sleep Scoring. Patterns, 3, Article ID: 100429. [Google Scholar] [CrossRef] [PubMed]
[43] Casciola, A.A., Carlucci, S.K., Kent, B.A., et al. (2021) A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data. Sensors, 21, Article No. 3316. [Google Scholar] [CrossRef] [PubMed]
[44] Zhao, D., Jiang, R., Feng, M., et al. (2022) A Deep Learning Algo-rithm Based on 1D CNN-LSTM for Automatic Sleep Staging. Technology and Health Care, Preprint, 1-14. [Google Scholar] [CrossRef] 
[45] Li, T., Zhang, B., Lv, H., et al. CAttSleepNet: Automatic End-to-End Sleep Staging Using Attention-Based Deep Neural Networks on Single-Channel EEG. International Journal of Envi-ronmental Research and Public Health, 19, Article No. 5199.[CrossRef] [PubMed]

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