Abstract：Sleep quality assessment and diagnosis highly depends on the doctors' effort and experience. It is quite labor intensive and time consuming for the doctor to inspect the long-term sleep monitoring records. The current automatic sleep staging mainly uses traditional machine learning and it highly relies on the features designed by experts. However, these features are usually incapable to capture the deep level features hidden in the measured data, and the behave not well for some staging such as N1. This paper proposed an automatic sleep staging algorithm based on multi-scale deep network. It used the deep network to automatically extract sleep signal features and used multi-scale analysis and discrimination criteria relating to the difficulty measure in the classification of different sleep stages. The classification results of stage W, N2, N3 and REM were selected and output in advance based on shallow layer features, and the fallible transition stage N1 entered a deeper network for further analysis. This policy improved the overall classification efficiency and especially the classification accuracy of the N1 stage. When extracting 197 sets of sample data for training and testing in the Sleep-EDFx data set and using only single-channel EEG signals, the average classification accuracy achieved 83%, and Kappa value was 0.749, which indicated that the constructed models were highly consistent. F1-score at stage N1 achieved 0.51. Compared with traditional machine learning algorithms and a variety of deep networks, the overall classification accuracy and accuracy of the N1 stage were improved. And at the same time, there was no apparent calculation increase. It is suitable for automatic real-time analysis.
柏浩冉, 张伟, 陆冠泽. 基于多尺度深度网络的自动睡眠分期[J]. 中国生物医学工程学报, 2021, 40(2): 170-176.
Bai Haoran, Zhang Wei, Lu Guanze. Multi-Scale Deep Network for Automatic Sleep Staging. Chinese Journal of Biomedical Engineering, 2021, 40(2): 170-176.
[1] Hori T, Sugita Y, Koga E, et al. Proposed supplements and amendments to “A manual of standardized terminology, techniques and scoring systems for sleep stages of human subjects”, the Rechtschaffen & Kales (1968) standard [J]. Psychiatry Clin Neurosci, 2008,55:305-310. [2] 王菡侨. 有关美国睡眠医学学会睡眠分期的最新判读标准指南解析 [J]. 诊断学理论与实践, 2009, 8(6):575-578. [3] Moody GB, Mark RG, Zoccola A, et al. Derivation of respiratory signals from multilead ECGs [J]. Computers in Cardiology, 1985, 12:35-45. [4] Moody GB, Mark RG, Bump MA, et al. Clinical validation of the ECG-Derived respiration (EDR) technique[J]. Computers in Cardiology, 1986, 13(2):87-92. [5] Varanini M, Emdin M, Allegri F, et al. Adaptive filtering of ECG signal for deriving respiratory activity[J]. Computers in Cardiology, 1990, 17:621-624. [6] Jitendran M. Spectral analysis methods for neurological signals[J]. Journal of Neuroscience Methods, 1998,83(1): 1-14. [7] 叶仙,胡洁,田畔,等. 基于精细复合多尺度熵与支持向量机的睡眠分期[J]. 上海交通大学学报, 2019, 53(3):321-326. [8] 倪红波,邓军权,施向南,等. 脉率变异性睡眠分期方法[J]. 浙江大学学报(工学版), 2017,51(3):149-153. [9] Chang Yenchun, Wu Sauhsuan; Tseng Liming, et al. AF detection by exploiting the spectral and temporal characteristics of ECG signals with the LSTM model [J]. Computing in Cardiology, 2018,45:1-4. [10] Kemp B, Zwinderman AH, Tuk B, et al. Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG [J]. IEEE Transactions on Biomedical Engineering,2000,47(9):1185-1194. [11] Goldberger A, Amaral L, Glass L, et al. PhysioBank,Physiotoolkit, and Physionet: Components of a new research resource for complex physiologic signals [J]. Circulation, 2000,101 (23):215-220. [12] Saadatnejad S, Oveisi M, Hashemi M. LSTM-based ECG classification for continuous monitoring on personal wearable devices [J]. IEEE Journal of Biomedical and Health Informatics, 2018, 24(2): 515-523. [13] Murugesan B, Ravichandran V, Ram K, et al. ECGNet: Deep network for arrhythmia classification[C]//2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA). Rome:IEEE,2018:1-6. [14] Lee W, Kim Y. Interactive sleep stage labelling tool for diagnosing sleep disorder using deep learning[C]// 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Honalulu:IEEE, 2018:183-186. [15] Mousavi S, Afghah F, Acharya UR. SleepEEGNet:Automated sleep stage scoring with sequence to sequence deep learning approach [J]. PLoS ONE,2019,14(5):1-15. [16] Akos V, Kiti M, Harri L. A compact deep learning network for temporal sleep stage classification [C]//2018 IEEE Life Sciences Conference (LSC). Montreal: IEEE, 2018:114-117. [17] Jeon Y, Kim S, Choi HS, et al. Pediatric sleep stage classification using multi-domain hybrid neural networks[J]. IEEE Access, 2019,7:96495-96505. [18] 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, 2017,25:1998-2008. [19] Hong H, Zhang L, Gu C, et al. Noncontact sleep stage estimation using a CW doppler radar[J]. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2018,8:260-270. [20] Xue B, Deng B, Hong H, et al. Non-contact sleep stage detection using canonical correlation analysis of respiratory sound[J]. IEEE Journal of Biomedical and Health Informatics, 2019,24(2):614-625. [21] Nochino T, Ohno Y, Kato T, et al. Sleep stage estimation method using a camera for home use[J]. Biomedical Engineering Letters, 2019,9(2):257-265. [22] Davies HJ, Nakamura T and Mandic DP, A transition probability based classification model for enhanced N1 sleep stage identification during automatic sleep stage scoring [C]//The 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Berlin: IEEE, 2019:3641-3644. [23] Huang G, Chen D, Li T, et al. Multi-scale dense convolutional networks for efficient image prediction [EB/OL]. https://arxiv.org/abs/1703.09844, 2018-06-07/2020-09-07. [24] 周志华.机器学习 [M]. 北京:清华大学出版社, 2016:31-35. [25] Cantor AB. Sample-size calculations for Cohen’s Kappa[J]. Psychological Methods, 1996, 1(2):150-153. [26] Chambon S, Galtier MN, Arnal PJ, et al. A deep learning architecture for temporal sleep stage classification using multivariate and time series[J]. IEEE Transactions on Neural Systems & Rehabilitation Engineering, 2018,26:758-769.
