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International Conference on Brain Informatics

BI 2020: Brain Informatics pp 361–371Cite as

EEG Based Sleep-Wake Classification Using JOPS Algorithm

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

Abstract

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.

Keywords

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

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

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  • DOI: 10.1007/978-3-030-59277-6_33
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Notes

  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.

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Acknowledgements

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

Author information

Authors and Affiliations

  1. Department of Electronics and Telecommunication Engineering (ETE), Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj, 8100, Bangladesh

    Abdullah Al-Mamun Bulbul

  2. Electronics and Communication Engineering Discipline, Khulna University, Khulna, 9208, Bangladesh

    Abdullah Al-Mamun Bulbul & Md. Abdul Awal

  3. Faculty of Health, Engineering and Sciences, University of Southern Queensland, Toowoomba, Australia

    Kumar Debjit

Authors
  1. Abdullah Al-Mamun Bulbul
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  2. Md. Abdul Awal
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  3. Kumar Debjit
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Corresponding author

Correspondence to Md. Abdul Awal .

Editor information

Editors and Affiliations

  1. Nottingham Trent University, Nottingham, UK

    Mufti Mahmud

  2. University of Padua, Padua, Italy

    Stefano Vassanelli

  3. Jahangirnagar University, Dhaka, Bangladesh

    M. Shamim Kaiser

  4. Maebashi Institute of Technology, Maebashi, Japan

    Prof. Dr. Ning Zhong

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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. https://doi.org/10.1007/978-3-030-59277-6_33

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  • DOI: https://doi.org/10.1007/978-3-030-59277-6_33

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