The Analysis and Classify of Sleep Stage Using Deep Learning Network from Single-Channel EEG Signal

  • Songyun Xie
  • Yabing Li
  • Xinzhou Xie
  • Wei Wang
  • Xu Duan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10637)


Electroencephalogram (EEG)-based sleep stage analysis is helpful for diagnosis of sleep disorder. However, the accuracy of previous EEG-based method is still unsatisfactory. In order to improve the classification performance, we proposed an EEG-based automatic sleep stage classification method, which combined convolutional neural network (CNN) and time-frequency decomposition. The time-frequency image (TFI) of EEG signals is obtained by using the smoothed short-time Fourier transform. The features derived from the TFI have been used as an input feature of a CNN for sleep stage classification. The proposed method achieves the best accuracy of 88.83%. The experimental results demonstrate that deep learning method provides better classification performance compared to other methods.


Convolutional neural networks (CNN) Time-frequency decomposition Sleep analysis 



This work was supported in part by National Natural Science Foundation of China (61273250), the Fundamental Research Funds for the Central Universities (No. 3102017jc11002) and the graduate starting seed fund of Northwestern Polytechnical University (Z2017141).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Songyun Xie
    • 1
  • Yabing Li
    • 1
  • Xinzhou Xie
    • 1
  • Wei Wang
    • 1
  • Xu Duan
    • 1
  1. 1.School of Electronics and InformationNorthwestern Polytechnical UniversityXi’anChina

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