Automatic sleep staging algorithm for stochastic depth residual networks based on transfer learning_Journal of Biomedical Engineering

Authors：

TIAN Yunzhi ^1,3 ,  ZHOU Qiang ^1,3 , LI Wan ^2,3

1. School of Electrical and Control Engineering, Shaanxi University of Science & Technology, Xi’an 710021, P. R. China;
2. School of Electronic Information and Artificial Intelligence, Shaanxi University of Science & Technology, Xi’an 710021, P. R. China;
3. Shaanxi Artificial Intelligence Joint Laboratory, Xi'an 710021, P. R. China;

Corresponding author：

ZHOU Qiang, Email: zhouqiang@sust.edu.cn

Keywords：

Automatic sleep staging; Stochastic depth; Transfer learning; Residual network; Continuous wavelet transform

DOI：

10.7507/1001-5515.202211021

Video：

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The existing automatic sleep staging algorithms have the problems of too many model parameters and long training time, which in turn results in poor sleep staging efficiency. Using a single channel electroencephalogram (EEG) signal, this paper proposed an automatic sleep staging algorithm for stochastic depth residual networks based on transfer learning (TL-SDResNet). Firstly, a total of 30 single-channel (Fpz-Cz) EEG signals from 16 individuals were selected, and after preserving the effective sleep segments, the raw EEG signals were pre-processed using Butterworth filter and continuous wavelet transform to obtain two-dimensional images containing its time-frequency joint features as the input data for the staging model. Then, a ResNet50 pre-trained model trained on a publicly available dataset, the sleep database extension stored in European data format (Sleep-EDFx) was constructed, using a stochastic depth strategy and modifying the output layer to optimize the model structure. Finally, transfer learning was applied to the human sleep process throughout the night. The algorithm in this paper achieved a model staging accuracy of 87.95% after conducting several experiments. Experiments show that TL-SDResNet50 can accomplish fast training of a small amount of EEG data, and the overall effect is better than other staging algorithms and classical algorithms in recent years, which has certain practical value.

Citation： TIAN Yunzhi, ZHOU Qiang, LI Wan. Automatic sleep staging algorithm for stochastic depth residual networks based on transfer learning. Journal of Biomedical Engineering, 2023, 40(2): 286-294. doi: 10.7507/1001-5515.202211021 Copy

1.	Sekkal R N, Bereksi-Reguig F, Ruiz-Fernandez D, et al. Automatic sleep stage classification: From classical machine learning methods to deep learning. Biomed Signal Process Control, 2022, 77: 103751.
2.	Capellini I, Mcnamara P, Preston T B, et al. Does sleep play a role in memory consolidation? A comparative test. PLoS One, 2017, 4(2): e4609.
3.	Wu Z S M, Chen J W, Huang Y M, et al. Effect of sleep disorders on the risks of cancers and site-specific cancers. Sleep Med, 2022, 100: 254-261.
4.	Zarpellon R S M, Vilela D R, Louzada F M, et al. Association of food intake with sleep disorders in children and adolescents with obesity. Sleep Med X, 2022, 4: 100053.
5.	Baiden P, Tadeo S K, Tonui B C, et al. Association between insufficient sleep and suicidal ideation among adolescents. Psychiat Res, 2020, 287: 112579.
6.	Chaput J P, Carrier J, Célyne B, et al. Economic burden of insufficient sleep duration in Canadian adults. Sleep health, 2022, 8(3): 298-302.
7.	Yang B F, Zhu X L, Liu Y T, et al. A single-channel EEG based automatic sleep stage classification method leveraging deep one-dimensional convolutional neural network and hidden Markov model. Biomed Signal Process Control, 2021, 68: 102581.
8.	Sarkar D, Guha D, Puspal T, et al. A comprehensive evaluation of contemporary methods used for automatic sleep staging. Biomed Signal Process Control, 2022, 77: 103819.
9.	Seifpour S, Niknazar H, Mohammad M, et al. A new automatic sleep staging system based on statistical behavior of local extrema using single channel EEG signal. Expert Syst Appl, 2018, 104: 277-293.
10.	Satapathy K S, Bhoi A K, Loganathan D, et al. Machine learning with ensemble stacking model for automated sleep staging using dual-channel EEG signal. Biomed Signal Process Control, 2021, 69: 102898.
11.	Tagluk M E, Sezgin N, Akin M. Estimation of sleep stages by an artificial neural network employing EEG, EMG and EOG. J Med Syst, 2010, 34(4): 717-725.
12.	Sors A, Bonnet S, Mirek S, et al. A convolutional neural network for sleep stage scoring from raw single-channel EEG. Biomed Signal Process Control, 2018, 42: 107-114.
13.	金峥, 贾克斌, 袁野. 基于混合注意力时序网络的睡眠分期算法研究. 生物医学工程学杂志, 2021, 38(2): 241-248.
14.	Abdollahpour M, Rezaii T Y, Farzamnia A, et al. Transfer learning convolutional neural network for sleep stage classification using two-stage data fusion framework. IEEE Access, 2020, 8: 180618-180632.
15.	Wang H, Guo H B, Zhang K, et al. Automatic sleep staging method of EEG signal based on transfer learning and fusion network. Neurocomputing, 2022, 488: 183-193.
16.	Heremans E R M, Phan H, Ansari A H, et al. Feature matching as improved transfer learning technique for wearable EEG. Biomed Signal Process Control, 2022, 78: 104009.
17.	仲伟峰, 李志, 刘燕, 等. 基于少导联脑电和时频深度网络的帕金森病伴快速眼动睡眠行为障碍智能辅助诊断方法. 生物医学工程学杂志, 2021, 38(6): 1043-1053.
18.	韩敏, 孙卓然. 基于小波变换和AdaBoost极限学习机的癫痫脑电信号分类. 计算机应用, 2015, 35(9): 2701-2705, 2709.
19.	赵杰, 张志明, 万灵燕, 等. 基于递归定量分析方法的孤独症儿童脑电信号特征提取与分类研究. 生物医学工程学杂志, 2021, 38(4): 663-670.
20.	宋颖, 梁磊, 王玥, 等. 基于改进EEMD和WVD联合时频分析的车轮多边形状态识别方法. 交通运输工程学报, 2021, 21(6): 259-268.
21.	张健钊, 姜威, 元辉, 等. 基于离散S变换和排列熵的癫痫脑电识别. 生物医学工程学杂志, 2017, 34(5): 681-687.
22.	杨杨, 李晓琴, 韩振波, 等. 基于三维多视角挤压激励卷积神经网络的肺结节良恶性分类研究. 生物医学工程学杂志, 2022, 39(3): 452-461.
23.	Yu X, Wang S H. Abnormality diagnosis in mammograms by transfer learning based on ResNet18. Fundam Inform, 2019, 168(2-4): 219-230.
24.	Huang G, Sun Y, Liu Z, et al. Deep networks with stochastic depth// The 16th European Conference on Computer Vision (ECCV 2016). Amsterdam: ECCV, 2016: 646-661.
25.	高爽, 徐巧枝. 迁移学习方法在医学图像领域的应用综述. 计算机工程与应用, 2021, 57(24): 39-50.
26.	Kemp B, Zwinderman A H, Tuk B, et al. Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE Trans Biomed Eng, 2000, 47(9): 1185-1194.
27.	Akara S, Dong H, Wu C, et al. DeepSleepNet: A model for automatic sleep stage scoring based on raw single-channel EEG. IEEE Trans Neural Syst Rehabil Eng, 2017, 25(11): 1998-2008.
28.	Sajad M, Fatemeh A, Rajendra A U. SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach. PLoS One, 2019, 14(5): e0216456.
29.	Pankaj J, Gaurav R, Debabrata D, et al. Automatic sleep stage classification using time–frequency images of CWT and transfer learning using convolution neural network. Biocybern Biomed Eng, 2020, 40(1): 495-504.

1. Sekkal R N, Bereksi-Reguig F, Ruiz-Fernandez D, et al. Automatic sleep stage classification: From classical machine learning methods to deep learning. Biomed Signal Process Control, 2022, 77: 103751.
2. Capellini I, Mcnamara P, Preston T B, et al. Does sleep play a role in memory consolidation? A comparative test. PLoS One, 2017, 4(2): e4609.
3. Wu Z S M, Chen J W, Huang Y M, et al. Effect of sleep disorders on the risks of cancers and site-specific cancers. Sleep Med, 2022, 100: 254-261.
4. Zarpellon R S M, Vilela D R, Louzada F M, et al. Association of food intake with sleep disorders in children and adolescents with obesity. Sleep Med X, 2022, 4: 100053.
5. Baiden P, Tadeo S K, Tonui B C, et al. Association between insufficient sleep and suicidal ideation among adolescents. Psychiat Res, 2020, 287: 112579.
6. Chaput J P, Carrier J, Célyne B, et al. Economic burden of insufficient sleep duration in Canadian adults. Sleep health, 2022, 8(3): 298-302.
7. Yang B F, Zhu X L, Liu Y T, et al. A single-channel EEG based automatic sleep stage classification method leveraging deep one-dimensional convolutional neural network and hidden Markov model. Biomed Signal Process Control, 2021, 68: 102581.
8. Sarkar D, Guha D, Puspal T, et al. A comprehensive evaluation of contemporary methods used for automatic sleep staging. Biomed Signal Process Control, 2022, 77: 103819.
9. Seifpour S, Niknazar H, Mohammad M, et al. A new automatic sleep staging system based on statistical behavior of local extrema using single channel EEG signal. Expert Syst Appl, 2018, 104: 277-293.
10. Satapathy K S, Bhoi A K, Loganathan D, et al. Machine learning with ensemble stacking model for automated sleep staging using dual-channel EEG signal. Biomed Signal Process Control, 2021, 69: 102898.
11. Tagluk M E, Sezgin N, Akin M. Estimation of sleep stages by an artificial neural network employing EEG, EMG and EOG. J Med Syst, 2010, 34(4): 717-725.
12. Sors A, Bonnet S, Mirek S, et al. A convolutional neural network for sleep stage scoring from raw single-channel EEG. Biomed Signal Process Control, 2018, 42: 107-114.
13. 金峥, 贾克斌, 袁野. 基于混合注意力时序网络的睡眠分期算法研究. 生物医学工程学杂志, 2021, 38(2): 241-248.
14. Abdollahpour M, Rezaii T Y, Farzamnia A, et al. Transfer learning convolutional neural network for sleep stage classification using two-stage data fusion framework. IEEE Access, 2020, 8: 180618-180632.
15. Wang H, Guo H B, Zhang K, et al. Automatic sleep staging method of EEG signal based on transfer learning and fusion network. Neurocomputing, 2022, 488: 183-193.
16. Heremans E R M, Phan H, Ansari A H, et al. Feature matching as improved transfer learning technique for wearable EEG. Biomed Signal Process Control, 2022, 78: 104009.
17. 仲伟峰, 李志, 刘燕, 等. 基于少导联脑电和时频深度网络的帕金森病伴快速眼动睡眠行为障碍智能辅助诊断方法. 生物医学工程学杂志, 2021, 38(6): 1043-1053.
18. 韩敏, 孙卓然. 基于小波变换和AdaBoost极限学习机的癫痫脑电信号分类. 计算机应用, 2015, 35(9): 2701-2705, 2709.
19. 赵杰, 张志明, 万灵燕, 等. 基于递归定量分析方法的孤独症儿童脑电信号特征提取与分类研究. 生物医学工程学杂志, 2021, 38(4): 663-670.
20. 宋颖, 梁磊, 王玥, 等. 基于改进EEMD和WVD联合时频分析的车轮多边形状态识别方法. 交通运输工程学报, 2021, 21(6): 259-268.
21. 张健钊, 姜威, 元辉, 等. 基于离散S变换和排列熵的癫痫脑电识别. 生物医学工程学杂志, 2017, 34(5): 681-687.
22. 杨杨, 李晓琴, 韩振波, 等. 基于三维多视角挤压激励卷积神经网络的肺结节良恶性分类研究. 生物医学工程学杂志, 2022, 39(3): 452-461.
23. Yu X, Wang S H. Abnormality diagnosis in mammograms by transfer learning based on ResNet18. Fundam Inform, 2019, 168(2-4): 219-230.
24. Huang G, Sun Y, Liu Z, et al. Deep networks with stochastic depth// The 16th European Conference on Computer Vision (ECCV 2016). Amsterdam: ECCV, 2016: 646-661.
25. 高爽, 徐巧枝. 迁移学习方法在医学图像领域的应用综述. 计算机工程与应用, 2021, 57(24): 39-50.
26. Kemp B, Zwinderman A H, Tuk B, et al. Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE Trans Biomed Eng, 2000, 47(9): 1185-1194.
27. Akara S, Dong H, Wu C, et al. DeepSleepNet: A model for automatic sleep stage scoring based on raw single-channel EEG. IEEE Trans Neural Syst Rehabil Eng, 2017, 25(11): 1998-2008.
28. Sajad M, Fatemeh A, Rajendra A U. SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach. PLoS One, 2019, 14(5): e0216456.
29. Pankaj J, Gaurav R, Debabrata D, et al. Automatic sleep stage classification using time–frequency images of CWT and transfer learning using convolution neural network. Biocybern Biomed Eng, 2020, 40(1): 495-504.

Journal of Biomedical Engineering

Automatic sleep staging algorithm for stochastic depth residual networks based on transfer learning

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