Automatic Sleep Staging Based on 3CNN-BiGRU

Abstract

Abstract: Aiming at the efficiency and accuracy of single-channel EEG signal sleep automatic staging, this paper proposes to use three-scale parallel Convolutional Neural Networks to extract sleep signal features and Bidirectional Gated Recurrent Unit 3CNN-BiGRU automatic sleep staging model to learn the internal time relationship between sleep stages. First, the model performs band-pass filtering on the original single-channel EEG signal, and uses the synthetic minority oversampling technique for class balance, and then sends it to the built model for training and verification experiments. Pre-training and fine-tuning training are used for optimizing the model, and 10-folds and 20-folds cross-validation is uses to improve training reliability. The experimental results of different models under different data sets show that the 3CNN-BiGRU model has achieved better training efficiency and better staging accuracy.

Key words: EEG signal, sleep staging, convolutional neural network, bidirectional gated recurrent unit, synthetic minority oversampling technique

TANG Jie, WEN Yuan-mei. Automatic Sleep Staging Based on 3CNN-BiGRU[J]. Computer and Modernization, 2022, 0(02): 120-126.

References ［23］

［1］	BUSONERA G, COGONI M, PULIGHEDDU M, et al. EEG spectral coherence analysis in nocturnal epilepsy［J］. IEEE Transactions on Biomedical Engineering, 2018,65(12):2713-2719.
［2］	KORKALAINEN H, AAKKO J, NIKKONEN S, et al. Accurate deep learning-based sleep staging in a clinical population with suspected obstructive sleep apnea［J］. IEEE Journal of Biomedical and Health Informatics, 2020,24(7):2073-2081.
［3］	YANG B, LIU H. Automatic identification of insomnia using optimal antisymmetric biorthogonal wavelet filter bank with ECG signals［J］. IEEE Access, 2020,8:104281.
［4］	ESTRADA E, NAZERAN H, NAVA P, et al. EEG feature extraction for classification of sleep stages［C］// Proceedings of the 26th Annual International Conference of the IEEE EMBS. 2004,1:196-199.
［5］	WULFF K, GATTI S, WETTSTEIN J G, et al. Sleep and circadian rhythm disruption in psychiatric and neurodegenerative disease［J］. Nature Reviews Neuroscience, 2010,11(8):589-599.
［6］	EBRAHIMI F, MIKAEILI M, ESTRADA E, et al. Automatic sleep stage classification based on EEG signals by using neural networks and wavelet packet coefficients［C］// 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2008:1151-1154.
［7］	罗志增,李亚飞,孟明,等. 脑电信号的混沌分析和小波包变换特征提取算法［J］. 仪器仪表学报, 2011,32(1):33-39.
［8］	范文兵,刘雪峰,赵艳阳. 基于单通道脑电信号的自动睡眠分期［J］. 计算机应用, 2017,37(z2):318-321.
［9］	ALICKOVIC E, SUBASI A. Ensemble SVM method for automatic sleep stage classification［J］. IEEE Transactions on Instrumentation and Measurement, 2018,67(6):1258-1265.
［10］	BASHA A J, BALAJI B S, POORNIMA S, et al. Support vector machine and simple recurrent network based automatic sleep stage classification of fuzzy kernel［J］. Journal of Ambient Intelligence and Humanized Computing, 2021,12:6189-6197.
［11］	TSINALIS O, MATTHEWS P M, GUO Y K, et al. Automatic sleep stage scoring with single-channel EEG using convolutional neural networks［J］. arXiv preprint arXiv:1610.01683, 2016.
［12］	WEI L J, LIN Y F, WANG J, et al. Time-frequency convolutional neural network for automatic sleep stage classification based on single-channel EEG［C］// 2017 IEEE 29th International Conference on Tools with Artificial Intelligence(ICTAI). 2017:88-95.
［13］	ANDREOTTI F, PHAN H, COORAY N, et al. Multichannel sleep stage classification and transfer learning using convolutional neural networks［C］// 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2018:171-174.
［14］	SUN C L, CHEN C, FAN J H, et al. A hierarchical sequential neural network with feature fusion for sleep staging based on EOG and RR signals［J］. Journal of Neural Engineering, 2019,16(6):066020.
［15］	LIU Y, FAN R T, LIU Y C. Deep identity confusion for automatic sleep staging based on single-channel EEG［C］// 2018 14th International Conference on Mobile Ad-Hoc and Sensor Networks(MSN). 2018:134-139.
［16］	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.
［17］	HOCHREITER S, SCHMIDHUBER J. Long short-term memory［J］. Neural Computation, 1997,9(8):1735-1780.
［18］	GERS F A, SCHMIDHUBER J, CUMMINS F. Learning to forget: Continual prediction with LSTM［J］. Neural Computation, 2000,12(10):2451-71.
［19］	MINH D L, SADEGHI-NIARAKI A, HUY H D, et al. Deep learning approach for short-term stock trends prediction based on two-stream gated recurrent unit network［J］. IEEE Access, 2018,6:55392-55404.
［20］	NIROSHANA I S M, ZHU X, CHEN Y, et al. Sleep stage classification based on EEG, EOG, and CNN-GRU deep learning model［C］// 2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST). 2019:1-7.
［21］	CHEN J X, JIANG D M, ZHANG Y N. A hierarchical bidirectional GRU model with attention for EEG-based emotion classification［J］. IEEE Access, 2019,7:118530-118540.
［22］	CHUNG J Y, GULCEHRE C, CHO K H, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling［J］. arXiv preprint arXiv:1412.3555, 2014.
［23］	CHAWLA N V, BOWYER K W, HALL L O, et al. SMOTE: Synthetic minority over-sampling technique［J］. Journal of Artificial Intelligence Research, 2002,16:341-378.

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