Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Pan, S.T., Kuo, C.E., Zeng, J.H., Liang, S.F.: A transition-constrained discrete hidden markov model for automatic sleep staging. BioMed. Eng. Online 11(1), 1–19 (2012)
Rechtschaffen, A.Q., Kales, A.A.: A manual of standardized terminology techniques and scoring system for sleep stages in human subjects. Psychiatry Clin. Neurosci. 26(6), 644 (1968)
Schulz, H.: Phasic or transient? Comment on the terminology of the AASM manual for the scoring of sleep and associated events. J. Clin. Sleep Med. JCSM Official Publ. Am. Acad. Sleep Med. 3(7), 752 (2007)
Aboalayon, K.A.I., Faezipour, M., Almuhammadi, W.S., Moslehpour, S.: Sleep stage classification using EEG signal analysis: a comprehensive survey and new investigation. Entropy 18(9), 272–303 (2016)
Herrera, L.J., Mora, A.M., Fernandes, C., Migotina, D., Guillen, A., Rosa, A.C.: Symbolic representation of the EEG for sleep stage classification. In: International Conference on Intelligent Systems Design and Applications 2011, vol. 60, pp. 253–258. IEEE, Cordoba, Spain (2011)
Ronzhina, M., Janoušek, O., Kolářová, J., Nováková, M., Honzík, P., Provazník, I.: Sleep scoring using artificial neural networks. Sleep Med. Rev. 16(3), 251–263 (2012)
Fraiwan, L., Lweesy, K., Khasawneh, N., Wenz, H., Dickhaus, H.: Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier. Comput. Methods Programs Biomed. 108(1), 10–19 (2012)
Hassan, A.R., Bashar, S.K., Bhuiyan, M.I.H.: Automatic classification of sleep stages from single-channel electroencephalogram. In: India Conference 2015, pp. 1–6. IEEE, New Delhi, India (2015)
Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2014)
Hinton, G., Deng, L., Yu, D., Dahl, G.E., Mohamed, A.R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T.N.: Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Sig. Process. Mag. 29(6), 82–97 (2012)
Lawrence, S., Giles, C.L., Tsoi, A.C., Back, A.D.: Face recognition: a convolutional neural-network approach. IEEE Trans. Neural Netw. 8(1), 98–113 (1997)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Kemp, B., Zwinderman, A.H., Tuk, B., Kamphuisen, H.A.C., Oberye, J.J.L.: Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE Trans. Biomed. Eng. 47(9), 1185–1194 (2000)
Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Kang, L., Ye, P., Li, Y., Doermann, D.: Convolutional neural networks for no-reference image quality assessment. In: Conference on Computer Vision and Pattern Recognition 2014, pp. 1733–1740. IEEE, Columbus, OH, USA (2014)
Lajnef, T., Chaibi, S., Ruby, P., Aguera, P.E., Eichenlaub, J.B., Samet, M., Kachouri, A., Jerbi, K.: Learning machines and sleeping brains: automatic sleep stage classification using decision-tree multi-class support vector machines. J. Neurosci. Methods 250(30), 94–105 (2015)
Acknowledgments
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Xie, S., Li, Y., Xie, X., Wang, W., Duan, X. (2017). The Analysis and Classify of Sleep Stage Using Deep Learning Network from Single-Channel EEG Signal. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10637. Springer, Cham. https://doi.org/10.1007/978-3-319-70093-9_80
Download citation
DOI: https://doi.org/10.1007/978-3-319-70093-9_80
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70092-2
Online ISBN: 978-3-319-70093-9
eBook Packages: Computer ScienceComputer Science (R0)