Skip to main content

EEG Based Sleep-Wake Classification Using JOPS Algorithm

  • 940 Accesses

Part of the Lecture Notes in Computer Science book series (LNAI,volume 12241)


Classification of sleep-wake is necessary for the diagnosis and treatment of sleep disorders, and EEG is normally used to assess sleep quality. Manual scoring is time-consuming and requires a sleep expert. Therefore, automatic sleep classification is essential. To accomplish this, features are extracted from the time domain, frequency domain, wavelet domain, and also from non-linear dynamics. In this study, a novel Jaya Optimization based hyper-Parameter and feature Selection (JOPS) algorithm is proposed to select optimal feature subset as well as hyper-parameters of the classifier such as KNN and SVM, simultaneously. JOPS is self-adaptive that automatically adapts to the population size. The proposed JPOS yielded the accuracy of 94.99% and 94.85% using KNN and SVM, respectively. JPOS algorithm is compared with genetic algorithm and differential evaluation-based feature selection algorithm. Finally, a decision support system is created to graphically visualize the sleep-wake state which will be beneficial to clinical staffs. Furthermore, the proposed JOPS can not only be used in sleep-wake classification but could be applied in other classification problems.


  • Sleep-Wake cycle
  • Hyper-parameter tuning
  • Feature Selection
  • JOPS algorithm
  • Classification
  • Decision Support System

A. A.-M. Bulbul and M. Abdul Awal—Equal Contributions.

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-59277-6_33
  • Chapter length: 11 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   79.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-59277-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   99.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.


  1. 1.

    In 10-fold cross validation, one-fold is used for testing and nine other folds are used for training and repeated ten times so that each fold i.e. whole dataset is tested.


  1. Littman, A.J., et al.: Sleep, ghrelin, leptin and changes in body weight during a 1-year moderate-intensity physical activity intervention. Int. J. Obes. 31, 466–475 (2007)

    CrossRef  Google Scholar 

  2. Park, H.-J., Oh, J.-S., Jeong, D.-U., Park, K.-S.: Automated sleep stage scoring using hybrid rule- and case-based reasoning. Comput. Biomed. Res. 33, 330–349 (2000)

    CrossRef  Google Scholar 

  3. Yeh, Z.-T., Chiang, R.P.-Y., Kang, S.-C., Chiang, C.-H.: Development of the insomnia screening scale based on ICSD-II. Int. J. Psychiatry Clin. Pract. 16, 259–267 (2012)

    CrossRef  Google Scholar 

  4. Leger, D., Pandi-Perumal, S., Healthcare, I.: Review of sleep disorders: their impact on public health. Public Health 30, 92161 (2007)

    Google Scholar 

  5. Ram, S., Seirawan, H., Kumar, S.K.S., Clark, G.T.: Prevalence and impact of sleep disorders and sleep habits in the United States. Sleep Breath. 14, 63–70 (2010).

    CrossRef  Google Scholar 

  6. Vaughn, B.V., Giallanza, P.: Technical review of polysomnography. Chest 134, 1310–1319 (2008)

    CrossRef  Google Scholar 

  7. Hassan, A.R., Bhuiyan, M.I.H.: Automatic sleep stage classification. In: 2nd International Conference on Electrical Information and Communication Technologies (EICT), pp. 211–216 (2015)

    Google Scholar 

  8. Hassan, A.R., Bhuiyan, M.I.H.: A decision support system for automatic sleep staging from EEG signals using tunable Q-factor wavelet transform and spectral features. J. Neurosci. Methods 271, 107–118 (2016)

    CrossRef  Google Scholar 

  9. Rodríguez-Sotelo, J.L., Osorio-Forero, A., Jiménez-Rodríguez, A., Cuesta-Frau, D., Cirugeda-Roldán, E., Peluffo, D.: Automatic sleep stages classification using EEG entropy features and unsupervised pattern analysis techniques. Entropy 16, 6573–6589 (2014)

    CrossRef  Google Scholar 

  10. Chen, K., Zhang, C., Ma, J., Wang, G., Zhang, J.: Sleep staging from single-channel EEG with multi-scale feature and contextual information. Sleep Breath. 23(4), 1159–1167 (2019).

    CrossRef  Google Scholar 

  11. Ghimatgar, H., Kazemi, K., Helfroush, M.S., Aarabi, A.: An automatic single-channel EEG-based sleep stage scoring method based on hidden Markov Model. J. Neurosci. Methods 324, 108320 (2019)

    CrossRef  Google Scholar 

  12. Tiwari, V., Jain, S.C.: An optimal feature selection method for histopathology tissue image classification using adaptive jaya algorithm. Evol. Intel. 1–14 (2019).

  13. Hassan, A.R., Bhuiyan, M.I.H.: Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting. Comput. Methods Programs Biomed. 140, 201–210 (2017)

    CrossRef  Google Scholar 

  14. Rahman, M.A., Hossain, M.A., Kabir, M.R., Sani, M.H., Abdullah Al, M., Awal, M.A.: Optimization of sleep stage classification using single-channel EEG signals. In: 2019 4th International Conference on Electrical Information and Communication Technology (EICT), pp. 1–6 (2019)

    Google Scholar 

  15. Rao, R.: Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int. J. Ind. Eng. Comput. 7, 19–34 (2016)

    Google Scholar 

  16. Teo, J.: Exploring dynamic self-adaptive populations in differential evolution. Soft. Comput. 10, 673–686 (2006).

    CrossRef  Google Scholar 

  17. Rao, R.V., Rai, D.P., Balic, J.: A multi-objective algorithm for optimization of modern machining processes. Eng. Appl. Artif. Intell. 61, 103–125 (2017)

    CrossRef  Google Scholar 

  18. Venkata Rao, R., Saroj, A.: A self-adaptive multi-population based Jaya algorithm for engineering optimization. Swarm Evol. Comput. 37, 1–26 (2017)

    CrossRef  Google Scholar 

  19. Khushaba, R.N., Al-Ani, A., Al-Jumaily, A.: Feature subset selection using differential evolution and a statistical repair mechanism. Expert Syst. Appl. 38, 11515–11526 (2011)

    CrossRef  Google Scholar 

  20. Siddiqi, U.F., Sait, S.M., Kaynak, O.: Genetic algorithm for the mutual information-based feature selection in univariate time series data. IEEE Access 8, 9597–9609 (2020)

    CrossRef  Google Scholar 

  21. Babatunde, O.H., Armstrong, L.: A genetic Algorithm-Based feature selection. Br. J. Math. Comput. Sci. 5, 889–905 (2014)

    Google Scholar 

Download references


This work is a part of the work supported by Khulna University Research Cell (KURC).

Author information

Authors and Affiliations


Corresponding author

Correspondence to Md. Abdul Awal .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Bulbul, A.AM., Abdul Awal, M., Debjit, K. (2020). EEG Based Sleep-Wake Classification Using JOPS Algorithm. In: Mahmud, M., Vassanelli, S., Kaiser, M.S., Zhong, N. (eds) Brain Informatics. BI 2020. Lecture Notes in Computer Science(), vol 12241. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59276-9

  • Online ISBN: 978-3-030-59277-6

  • eBook Packages: Computer ScienceComputer Science (R0)