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Mukherjee S., Patel S. R., Kales S. N., Ayas N. T., Strohl K. P., Gozal D., Malhotra A., 2015, An official American Thoracic Society statement: the importance of healthy sleep. Recommendations and future priorities., American journal of respiratory and critical care medicine, Vol. 191, No. 12DOI
Chokroverty S., 2010, Overview of sleep & sleep disorders, Indian J. Med. Res., Vol. 131, No. 2, pp. 126-140Google Search
Krystal A. D., Edinger J. D., 2008, Measuring sleep quality, Sleep Med., Vol. 9, No. suppl. 1, pp. 10-17DOI
Berry R. B., et al. , 2017, AASM scoring manual updates for 2017 (version 2.4), J. Clin. Sleep Med., Vol. 13, No. 5, pp. 665-666DOI
Whitney C. W., et al. , 1998, Reliability of scoring respiratory disturbance indices and sleep staging, Sleep, Vol. 21, No. 7, pp. 749-757DOI
Fraiwan L., Lweesy K., Khasawneh N., Wenz H., Dickhaus H., 2012, Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier, Comput. Methods Programs Biomed., Vol. 108, No. 1, pp. 10-19DOI
Shen X., Fan Y., 2012, Sleep stage classification based on eeg signals by using improved hilbert-huang transform, Appl. Mech. Mater., Vol. 138-139, pp. 1096-1101DOI
Supratak A., Dong H., Wu C., Guo Y., 2017, DeepSleepNet: A model for automatic sleep stage scoring based on raw single-channel EEG, IEEE Trans. Neural Syst. Rehabil. Eng., Vol. 25, No. 11, pp. 1998-2008DOI
Phan H., Andreotti F., Cooray N., Oliver Chen Y., De Vos M., 2018, DNN Filter Bank Improves 1-Max Pooling CNN for Single-Channel EEG Automatic Sleep Stage Classification, Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, Vol. 2018-july, pp. 453-456DOI
Zhu T., Luo W., Yu F., 2020, Convolution-and attention-based neural network for automated sleep stage classification, Int. J. Environ. Res. Public Health, Vol. 17, No. 11, pp. 1-13DOI
Ismail Fawaz H., et al. , 2020, InceptionTime: Finding AlexNet for time series classification, Data Min. Knowl. Discov., Vol. 34, No. 6, pp. 1936-1962DOI
M. A., Alex Graves G. H., 2013, Speech Recognition with Deep Recurrent Neural Networks, Department of Computer Science, University of Toronto, Dep. Comput. Sci. Univ. Toronto, Vol. 3, No. 3, pp. 45-49DOI
Dietterich, Thomas G. , 2002, Ensemble learning The handbook of brain theory and neural networks 2.1, pp. 110-125Google Search
Luo W., Li Y., Urtasun R., Zemel R., 2016, Understanding the effective receptive field in deep convolutional neural networks, Adv. Neural Inf. Process. Syst., no. Nips, pp. 4905-4913URL
Goldberger A. L., Amaral L. A., Glass L., Hausdorff J. M., Ivanov P. C., Mark R. G., Stanley H. E., 2000, PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals., circulation 101.23.DOI
T. Hori, et al. , 2001, Proposed supplements and amendments to ‘A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects’, the Rechtschaffen & Kales (1968) standard, Psychiatry Clin. Neurosci., Vol. 55, No. 3, pp. 305-310DOI
Stone M., 1974, Cross-Validatory Choice and Assessment of Statistical Predictions, J. R. Stat. Soc. Ser. B, Vol. 36, No. 2, pp. 111-133DOI
Tsinalis O., Matthews P. M., Guo Y., 2016, Automatic Sleep Stage Scoring Using Time-Frequency Analysis and Stacked Sparse Autoencoders, Ann. Biomed. Eng., Vol. 44, No. 5, pp. 1587-1597DOI
Seo H., Back S., Lee S., Park D., Kim T., Lee K., 2020, Intra- and inter-epoch temporal context network (IITNet) using sub-epoch features for automatic sleep scoring on raw single-channel EEG, Biomed. Signal Process. Control, Vol. 61DOI
Eldele E., et al. , 2021, An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG, in IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 29, pp. 809-818DOI