Skip to main content
SpringerLink
Log in

Sleep Stages Classification Using Neural Networks with Multi-channel Neural Data

  • Conference paper
  • First Online:
Brain Informatics and Health (BIH 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9250))

Included in the following conference series:

Abstract

Recent studies on sleep reveal its importance not only on body functioning but also in brain and cognition health. As the development of wearable smart devices, neural data such as electroencephalogram (EEG) becomes commonly accessible. Developing good algorithm for sleep classification is more urgent and necessary than before. This paper presents a new and robust 5-stage sleep classification algorithm based on feedforward neural network, with flexible model structure using normalized Power Spectral Density (PSD) collected from multi-channel neural data. The algorithm is described with detailed mathematical elaboration. It utilizes the power of deep learning and provides average accuracy close to 90% which is competitive, compared with previous researches.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. What are REM and non-REM sleep? http://www.webmd.com/sleep-disorders/guide/sleep-101 (accessed June 01, 2015)

  2. Cao, G., Guo, Y., Bouman, C.A.: High dimensional regression using the sparse matrix transform (SMT), pp. 1870–1873 (2010)

    Google Scholar 

  3. Carskadon, M.A., Dement, W.C., et al.: Normal human sleep: an overview. Principles and Practice of Sleep Medicine 2, 16–25 (2000)

    Google Scholar 

  4. Deng, L., Yu, D.: Deep learning: methods and applications. Foundations and Trends in Signal Processing 7(3–4), 197–387 (2014)

    Article  MathSciNet  Google Scholar 

  5. Durrant, S.J., Cairney, S.A., Lewis, P.A.: Overnight consolidation aids the transfer of statistical knowledge from the medial temporal lobe to the striatum. Cerebral Cortex 23(10), 2467–2478 (2013)

    Article  Google Scholar 

  6. Ebrahimi, F., Mikaeili, M., Estrada, E., Nazeran, H.: Automatic sleep stage classification based on eeg signals by using neural networks and wavelet packet coefficients. In: 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2008, pp. 1151–1154. IEEE (2008)

    Google Scholar 

  7. Estrada, E., Nazeran, H., Barragan, J., Burk, J., Lucas, E., Behbehani, K.: Eog and emg: two important switches in automatic sleep stage classification. In: 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2006, pp. 2458–2461. IEEE (2006)

    Google Scholar 

  8. Ge, Z.: Zhenhao Ge’s academic homepage - software (2015). https://sites.google.com/site/gezhenhao/software

  9. Ge, Z.: Development of Automatic Speech Evaluation System. Ph.D. thesis, Purdue University Indianapolis (2008)

    Google Scholar 

  10. Ge, Z.: Mispronunciation detection for language learning and speech recognition adaptation (2013)

    Google Scholar 

  11. Ge, Z., Sharma, S.R., Smith, M.J.: Adaptive frequency cepstral coefficients for word mispronunciation detection. In: 2011 4th International Congress on Image and Signal Processing (CISP), vol. 5, pp. 2388–2391. IEEE (2011)

    Google Scholar 

  12. Ge, Z., Sharma, S.R., Smith, M.J.: PCA method for automated detection of mispronounced words. In: SPIE Defense, Security, and Sensing, pp. 80581D–80581D. International Society for Optics and Photonics (2011)

    Google Scholar 

  13. Ge, Z., Sharma, S.R., Smith, M.J.: PCA/LDA approach for text-independent speaker recognition. In: Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol. 8401, p. 7 (2012)

    Google Scholar 

  14. Ge, Z., Sharma, S.R., Smith, M.J.: Improving mispronunciation detection using adaptive frequency scale. Computers & Electrical Engineering 39(5), 1464–1472 (2013)

    Article  Google Scholar 

  15. Guo, Y., Depalov, D., Bauer, P., Bradburn, B., Allebach, J.P., Bouman, C.A.: Binary image compression using conditional entropy-based dictionary design and indexing. In: Proc. SPIE, Color Imaging: Displaying, Processing, Hardcopy, and Applications, vol. 8652 (2013)

    Google Scholar 

  16. Hinton, G., Deng, L., Yu, D., Dahl, G.E., Mohamed, A.R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T.N., et al.: Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine 29(6), 82–97 (2012)

    Article  Google Scholar 

  17. Lu, C., Allebach, J.P., Wagner, J., Pitta, B., Larson, D., Guo, Y.: Online image classification under monotonic decision boundary constraint. In: Proc. SPIE, Color Imaging: Displaying, Processing, Hardcopy, and Applications, vol. 9395 (2015)

    Google Scholar 

  18. Ng, A.: Cousera course: Machine learning. https://www.coursera.org/learn/machine-learning/home/info (accessed June 01, 2015)

  19. Rasmussen, C.E.: Gaussian processes for machine learning (2006)

    Google Scholar 

  20. Šušmáková, K.: Human sleep and sleep eeg. Measurement Science Review 4(2), 59–74 (2004)

    Google Scholar 

  21. Tagluk, M.E., Sezgin, N., Akin, M.: Estimation of sleep stages by an artificial neural network employing eeg, emg and eog. Journal of Medical Systems 34(4), 717–725 (2010)

    Article  Google Scholar 

  22. Welch, P.D.: The use of fast fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms. IEEE Transactions on Audio and Electroacoustics 15(2), 70–73 (1967)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Purdue University, West Lafayette, IN, USA

    Zhenhao Ge & Yufang Sun

Authors
  1. Zhenhao Ge
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yufang Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhenhao Ge .

Editor information

Editors and Affiliations

  1. Imperial College London , Data Science Institute, London, United Kingdom

    Yike Guo

  2. Institute of Cognitive Neuroscience, University College London, London, United Kingdom

    Karl Friston

  3. Imperial College London, London, United Kingdom

    Faisal Aldo

  4. Campus Biotech, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland

    Sean Hill

  5. Allen Institute for Brain Science, Seattle, USA

    Hanchuan Peng

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Ge, Z., Sun, Y. (2015). Sleep Stages Classification Using Neural Networks with Multi-channel Neural Data. In: Guo, Y., Friston, K., Aldo, F., Hill, S., Peng, H. (eds) Brain Informatics and Health. BIH 2015. Lecture Notes in Computer Science(), vol 9250. Springer, Cham. https://doi.org/10.1007/978-3-319-23344-4_30

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-23344-4_30

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23343-7

  • Online ISBN: 978-3-319-23344-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation