Abstract
When humans fall asleep, they go through five sleep stages, i.e. wakefulness, stages of non-rapid eye movement consisting of N1, N2 and N3, and rapid eye movement (REM). Monitoring the proportion and distribution of sleep stages can help to diagnose sleep disorder and measure sleep quality. Traditional process of sleep scoring by well-trained experts is quite subjective and time-consuming. Automatic sleep staging analysis has demonstrated a lot of usefulness and attracted increasing attentions. With the massively growing size of accessible data and the rapid development of computational power, Deep Learning (DL) has achieved significant improvement in a lot of areas. In this work, an intelligent system for sleep stage classification is developed by using polysomnographic (PSG) data including electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) based on a DL architecture. In our method, the Convolutional Neural Network (CNN) is employed as the feature detector, which is combined with a Hidden Markov Model (HMM) for its strengths of dealing with temporal data. Experiment results have shown a performance improvement compared to those methods with hand-crafted features or unsupervised feature learning by Deep Brief Learning (DBN).
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
Rechtschaffen, A., Kales, A.: A Manual of standardized terminology, techniques and scoring system for sleep stages of human subjects, pp. 3–7. Government Printing Office, Public Health Service, Washington DC (1968)
Iber, C., Ancoli-Israel, S., Chesson, A.L., Quan, S.F.: The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology, and Technical Specifications. American Academy of Sleep Medicine, Westchester (2007)
Carskadon, M.A., Rechtschaifen, A.: Monitoring and staging human sleep. In: Kryger, M.H., Rpth, T., Dement, W.C. (eds.) Principles and Practice of Sleep Medicine, 4th edn. Saunders Elsevier (2005)
Zhang, J.M., Wu, Y., Bai, J., Chen, F.Q.: Automatic sleep stage classification based on sparse deep belief net and combination of multiple classifiers. Trans. Inst. Measure. Control 38(4), 435–451 (2016)
Langkvist, M., Karlsson, L., Loutfi, A.: Sleep stage classification using unsupervised feature learning. Advances in Artificial Neural Systems, vol. 2012, 9 pages (2012), http://dx.doi.org/10.1155/2012/107046
Yulita, I.N., Fanany, M.I., Arymurthy, A.M.: Sequence-based sleep stage classification using conditional neural fields. In: Computational and Mathematical Methods in Medicine. Hindawi Publishing (2016)
Giri, E.P., Fanany, M.I., Arymurthy, A.M.: Combining Generative and Discriminative Neural Networks for Sleep Stages Classification. In: Computational Intelligence and Neuroscience. Hindawi Publishing (2016)
Tsinalis, O., Mathews, P.M., Guo, Y.: Automatic sleep stage scoring using time-frequency analysis and stacked sparse autoencoders. Ann. Biomed. Eng. (2015)
Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. Aistats 15, 315–323 (2011)
Supratak, A., Wu, C., Dong, H., Sun, K., Guo, Y.: Survey on feature extraction and applications of biosignals. In: Holzinger, A. (ed.) Machine Learning for Health Informatics. LNCS (LNAI), vol. 9605, pp. 161–182. Springer, Cham (2016). doi:10.1007/978-3-319-50478-0_8
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313, 504–507 (2006)
Deng, L.: Three classes of deep learning architectures and their applications: a tutorial survey. APSIPA Trans. Sig. Inf. Process. (2012)
Goldberger, A.L., Amaral, L.A., Glass, L., et al.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), 215–220 (2000)
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grants 61573150 and 61573152, Guangdong innovative project 2013KJCX0009, Guangzhou project 201604016113 and 201604046018.
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
Cen, L., Yu, Z.L., Tang, Y., Shi, W., Kluge, T., Ser, W. (2017). Deep Learning Method for Sleep Stage Classification. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10635. Springer, Cham. https://doi.org/10.1007/978-3-319-70096-0_81
Download citation
DOI: https://doi.org/10.1007/978-3-319-70096-0_81
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70095-3
Online ISBN: 978-3-319-70096-0
eBook Packages: Computer ScienceComputer Science (R0)