Skip to main content

Deep Sleep Recognition Based on CNNs and Data Augmentation

  • Conference paper
  • First Online:
Artificial Intelligence Applications and Innovations (AIAI 2024)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 714))

  • 350 Accesses

Abstract

Deep sleep is a key part of the sleep cycle and plays a crucial role in the daily physical recovery process. Due to the complexity and lengthy process of collecting sleep EEG data in real life, which can also pose unnecessary inconvenience to subjects over long periods, we employed three data augmentation methods to enrich the dataset and introduce more variability under limited data capabilities. In this paper, we explore the application of data augmentation in classifying deep sleep stages by analyzing electroencephalogram (EEG) signals with Convolutional Neural Networks (CNNs). These strategies are designed to present the machine learning models with a broader range of sleep EEG signal features, thereby enhancing their ability to accurately identify deep sleep stages. The study employed three publicly available CNN models that are effective for sleep stage classification and utilizes the Sleep-EDF public dataset for validation. The research findings indicate that datasets augmented with the proposed techniques show higher classification accuracy across all models, confirming the effectiveness and potential of these data augmentation methods in the context of deep sleep stage identification.

Supported by JSPS KAKENHI 20H04249.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Chen, R., Sui, L., Xia, M., Liu, J., Zhang, T., Cao, J.: Convolutional neural networks for deep sleep detection based on data augmentation. Int. J. Comput. Technol. 24, 1–13 (2024)

    Article  Google Scholar 

  2. Barnes, C.M., Lucianetti, L., Bhave, D.P., Christian, M.S.: “You wouldn’t like me when I’m sleepy’’: leaders’ sleep, daily abusive supervision, and work unit engagement. Acad. Manag. J. 58(5), 1419–1437 (2015). https://doi.org/10.5465/amj.2013.1063

    Article  Google Scholar 

  3. Teplan, M., et al.: Fundamentals of EEG measurement. Measur. Sci. Rev. 2(2), 1–11 (2002)

    Google Scholar 

  4. Michel, C.M., Murray, M.M.: Towards the utilization of EEG as a brain imaging tool. Neuroimage 61(2), 371–385 (2012). https://doi.org/10.1016/j.neuroimage.2011.12.039

    Article  Google Scholar 

  5. Knoop, M.S., de Groot, E.R., Dudink, J.: Current ideas about the roles of rapid eye movement and non-rapid eye movement sleep in brain development. Acta Paediatr. 110(1), 36–44 (2021)

    Article  Google Scholar 

  6. Carskadon, M.A., Dement, W.C., et al.: Normal human sleep: an overview. Princ. Pract. Sleep Med. 4(1), 13–23 (2005)

    Article  Google Scholar 

  7. Ding, X., He, Q.: Energy-fluctuated multiscale feature learning with deep convnet for intelligent spindle bearing fault diagnosis. IEEE Trans. Instrum. Meas. 66(8), 1926–1935 (2017)

    Article  Google Scholar 

  8. Pan, J., Sayrol, E., Giro-i-Nieto, X., McGuinness, K., O’Connor, N.E.: Shallow and deep convolutional networks for saliency prediction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 598–606 (2016)

    Google Scholar 

  9. Kim, S.-J., Lee, D.-H., Lee, S.-W.: Rethinking CNN architecture for enhancing decoding performance of motor imagery-based EEG signals. IEEE Access 10, 96984–96996 (2022)

    Article  Google Scholar 

  10. Lawhern, V.J., Solon, A.J., Waytowich, N.R., Gordon, S.M., Hung, C.P., Lance, B.J.: EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces. J. Neural Eng. 15(5), 056013 (2018). https://doi.org/10.1088/1741-2552/aace8c

    Article  Google Scholar 

  11. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995). https://doi.org/10.1007/BF00994018

    Article  Google Scholar 

  12. Guresen, E., Kayakutlu, G.: Definition of artificial neural networks with comparison to other networks. Procedia Comput. Sci. 3, 426–433 (2011). https://doi.org/10.1016/j.procs.2010.12.071

    Article  Google Scholar 

  13. Ho, T.K.: Proceedings of 3rd International Conference on Document Analysis and Recognition, pp. 278–282. IEEE (1995)

    Google Scholar 

  14. Antoniou, A., Storkey, A., Edwards, H.: Data augmentation generative adversarial networks. arXiv preprint arXiv:1711.04340 (2017)

  15. Kassam, S.A.: Signal Detection in Non-Gaussian Noise. Springer, Heidelberg (2012)

    Google Scholar 

  16. Kiranyaz, S., Avci, O., Abdeljaber, O., Ince, T., Gabbouj, M., Inman, D.J.: 1D convolutional neural networks and applications: a survey. Mech. Syst. Signal Process. 151, 107398 (2021)

    Article  Google Scholar 

  17. Zhu, Y., Li, Y., Lu, J., Li, P.: EEGNet with ensemble learning to improve the cross-session classification of SSVEP based BCI from ear-EEG. IEEE Access 9, 15295–15303 (2021)

    Article  Google Scholar 

  18. Anguita, D., Ghelardoni, L., Ghio, A., Oneto, L., Ridella, S., et al.: The ‘K’ in K-fold cross validation. In: ESANN, pp. 441–446 (2012)

    Google Scholar 

  19. Yadav, S., Shukla, S.: Analysis of k-fold cross-validation over hold-out validation on colossal datasets for quality classification. In: 2016 IEEE 6th International Conference on Advanced Computing (IACC), pp. 78–83 (2016)

    Google Scholar 

  20. 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). https://doi.org/10.1109/10.867928

    Article  Google Scholar 

  21. Mourtazaev, M.S., Kemp, B., Zwinderman, A.H., Kamphuisen, H.A.C.: Age and gender affect different characteristics of slow waves in the sleep EEG. Sleep 18(7), 557–564 (1995). https://doi.org/10.1093/sleep/18.7.557

    Article  Google Scholar 

  22. Kemp, B., Olivan, J.: European data format ‘plus’(EDF+), an EDF alike standard format for the exchange of physiological data. Clin. Neurophysiol. 114(9), 1755–1761 (2003). https://doi.org/10.1016/S1388-2457(03)00123-8

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by JSPS KAKENHI 20H04249.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ruixuan Chen .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 IFIP International Federation for Information Processing

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chen, R., Sui, L., Xia, M., Cao, J. (2024). Deep Sleep Recognition Based on CNNs and Data Augmentation. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Avlonitis, M., Papaleonidas, A. (eds) Artificial Intelligence Applications and Innovations. AIAI 2024. IFIP Advances in Information and Communication Technology, vol 714. Springer, Cham. https://doi.org/10.1007/978-3-031-63223-5_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-63223-5_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-63222-8

  • Online ISBN: 978-3-031-63223-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics