[1] Luyster FS, Strollo Jr PJ, Zee PC, et al. Sleep: a health imperative[J]. Sleep, 2012, 35(6): 727-734. [2] Finan PH, Quartana PJ, Remeniuk B, et al. Partial sleep deprivation attenuates the positive affective system: effects across multiple measurement modalities[J]. Sleep, 2017, 40(1): zsw017. [3] Memar P, Faradji F. A novel multi-class EEG-based sleep stage classification system[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018, 26(1): 84-95. [4] Lin YP, Jung TP. Improving EEG-based emotion classification using conditional transfer learning[J]. Frontiers in Human Neuroscience, 2017,11: 334. [5] Tsinalis O, Matthews PM, Guo Y, et al. Automatic sleep stage scoring with single-channel EEG using convolutional neural networks[EB/OL]. (2016-10-05)[2022-04-29]. https://arxiv.org/abs/1610.01683 [6] Chambon S, Galtier MN, Arnal PJ, et al. A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018, 26(4): 758-769. [7] Supratak A, Dong H, Wu C, et al. DeepSleepNet: a model for automatic sleep stage scoring based on raw single-channel EEG[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017, 25(11): 1998-2008. [8] Mousavi S, Afghah F, Acharya UR. SleepEEGNet: automated sleep stage scoring with sequence to sequence deep learning approach[J]. PLoS One, 2019, 14(5): e0216456. [9] Chawla NV, Bowyer KW, Hall LO, et al. SMOTE: synthetic minority over-sampling technique[J]. Journal of Artificial Intelligence Research, 2002, 16: 321-357. [10] Lin TY, Goyal P, Girshick R, et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(2): 318-327.? [11] Schirrmeister RT, Springenberg JT, Fiederer LDJ, et al. Deep learning with convolutional neural networks for EEG decoding and visualization[J]. Human Brain Mapping, 2017, 38: 5391-5420. [12] Eldele E, Chen Z, Liu C, et al. An attention-based deep learning approach for sleep stage classification with single-channel EEG[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2021, 29: 809-818. [13] Huang W, Cheng J, Yang Y, et al. An improved deep convolutional neural network with multi-scale information for bearing fault diagnosis[J]. Neurocomputing, 2019, 359: 77-92. [14] Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[C]// 31st Conference on Neural Information Processing Systems(NIPS 2017). Long Beach, CA, USA: NIPS, 2017: 5998-6008. [15] Kingma DP, Ba J. Adam: a method for stochastic optimization[C]//3rd International Conference on Learning Representations. San Diego, USA: ICLR, 2015: 1?15. [16] Lawhern VJ, Solon AJ, Waytowich NR, et al. EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces[J]. Journal of Neural Engineering, 2018, 15(5): 056013. [17] Goldberger AL, Amaral LA, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals[J]. Circulation, 2000, 101(23): E215-E220.
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