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Automated Classification of Sleep Stages Based on Electroencephalogram Signal Using Machine Learning Techniques

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Intelligent Systems

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

Sleep disorder is one of the major challenges across the globe. The main diagnosis step is a polysomnography test for various types of sleep disorders. But it has been seen that this PSG test takes more time and human interpretation is also required, and ultimately it produces biased results. Therefore, we propose an automated sleep staging system for five-sleep stages classification. The main intention of this research work is an improvement on the classification accuracy for five-sleep states. We considered both linear and nonlinear properties from the input sleep recordings. The performance of the proposed model is evaluated using the ISRUC-Sleep subgroup-I dataset. The model achieved an overall accuracy of 98.60% using testing data. The proposed sleep staging system is ready for clinical practices in the real-time applications.

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Author information

Authors and Affiliations

  1. Pondicherry Engineering College, Puducherry, 605014, India

    Santosh Kumar Satapathy & D. Loganathan

  2. Godavari Institute of Engineering and Technology, Rajahmundry, Andhra Pradesh, 533296, India

    M. V. Sangameswar

  3. Vignana Bharathi Institute of Technology, Medchal, Telangana, 501301, India

    Deepika Vodnala

Authors
  1. Santosh Kumar Satapathy
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  2. D. Loganathan
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  3. M. V. Sangameswar
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  4. Deepika Vodnala
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Corresponding author

Correspondence to Santosh Kumar Satapathy .

Editor information

Editors and Affiliations

  1. Artificial Intelligence Institute, University of South Carolina, Columbia, SC, USA

    Amit Sheth

  2. JK Lakshmipat University, Jaipur, India

    Amit Sinhal

  3. University of Maryland, College Park, MD, USA

    Abhinav Shrivastava

  4. BeingAI Limited/Socients AI and Robotics, Paris, France

    Amit Kumar Pandey

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Satapathy, S.K., Loganathan, D., Sangameswar, M.V., Vodnala, D. (2021). Automated Classification of Sleep Stages Based on Electroencephalogram Signal Using Machine Learning Techniques. In: Sheth, A., Sinhal, A., Shrivastava, A., Pandey, A.K. (eds) Intelligent Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-2248-9_39

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