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Automated Classification of Sleep Stages Using Single-Channel EEG Signal: A Machine Learning-Based Method

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Advances in Computing and Data Sciences (ICACDS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1614))

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Abstract

One of the major contributors to improper sleep patterns is the rapidly occurring changes in today’s lifestyle. We aimed to develop an automated algorithm based on to classify the sleep stages during sleep hours. Maintaining such unhealthy sleep patterns for a longer period may lead to different neurological disorders. Delay in diagnosis further worsens the condition and leads to other serious health issues. The first step in analyzing any sleep-based abnormalities is the proper classification of the sleep stages. The proposed study obtains, a single-modal channel of electroencephalogram (EEG) signals as input to the model. The main objective is to screen the pertinent features which can assist in identifying the irregularities that occurred during sleep hours. The entire experiment was carried out on two different subgroups of the ISRUC-Sleep dataset and finally, we considered the support vector machine (SVM) for the classification of sleep stages. The proposed model yielded the best classification accuracy of 97.73%, and 96.51% with subgroup-I, and subgroup-III subjects, respectively. The proposed model is effective for automated multi-class sleep state classification method is developed for different medical-conditioned subjects. Compared to gold standard polysomnography, our algorithm doesn’t require any additional electrodes and which are especially valuable in improving the sleep staging classification performance.

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Author information

Authors and Affiliations

  1. Department of Information and Communication Technology, Pandit Deendayal Energy University (PDEU), Gandhinagar, India

    Santosh Satapathy

  2. Department of Electrical and Electronics Engineering, Gandhi Institute of Science and Technology, Rayagada, Odisha, India

    Shrinibas Pattnaik & Badal Acharya

  3. Department of Computer Science and Engineering, IIIT, Chittoor, Andhra Pradesh, India

    Rama Krushna Rath

Authors
  1. Santosh Satapathy
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  2. Shrinibas Pattnaik
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  3. Badal Acharya
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  4. Rama Krushna Rath
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Corresponding author

Correspondence to Santosh Satapathy .

Editor information

Editors and Affiliations

  1. University of KwaZulu-Natal, Durban, South Africa

    Mayank Singh

  2. Jaypee University of Engineering and Technology, Guna, India

    Vipin Tyagi

  3. Jaypee University of Information Technology, Waknaghat, India

    P. K. Gupta

  4. Institute of Information Theory and Automation, Prague, Czech Republic

    Jan Flusser

  5. University of Ottawa, Ottawa, ON, Canada

    Tuncer Ören

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Satapathy, S., Pattnaik, S., Acharya, B., Rath, R.K. (2022). Automated Classification of Sleep Stages Using Single-Channel EEG Signal: A Machine Learning-Based Method. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2022. Communications in Computer and Information Science, vol 1614. Springer, Cham. https://doi.org/10.1007/978-3-031-12641-3_20

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  • DOI: https://doi.org/10.1007/978-3-031-12641-3_20

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-12641-3

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