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Exploring Cepstral Coefficient Based Sleep Stage Scoring Method for Single-Channel EEG Signal Using Machine Learning Technique

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Advances in Signal Processing and Intelligent Recognition Systems (SIRS 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 678))

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Abstract

Sleep stage scoring is a critical task where conventionally large volume of data has to be analyzed visually which is troublesome, time-consuming and error prone. Eventually, machine learning technique is required for automatic sleep stage scoring. Therefore, a new feature extraction method for EEG analysis and classification is discussed based on the statistical properties of cepstral coefficients. The sleep EEG signal is segmented into 30 s epoch and each epoch is decomposed into different frequency bands: Gamma (γ), Beta (β), Alpha (α), Theta (θ) and Delta (δ) by employing the Discrete Wavelet Transform (DWT). The statistical properties of Mel Frequency Cepstral Coefficients (MFCCs), which represent the short term spectral characteristics of the wavelet coefficients, are extracted. The MFCC feature vectors are incorporated into the Gaussian Mixture Model with Expectation Maximization (GMM-EM) to classify various sleep stages: Wake, Rapid Eye Movement (REM) and Non-Rapid Eye Movement (N-REM) stage1 (S1), N-REM stage2 (S2), N-REM stage3 (S3), N-REM stage4 (S4). The proposed feature extraction for sleep stage scoring achieves 88.71% of average classification accuracy.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Velammal Engineering College, Surapet, Chennai, India

    S. Rajalakshmi

  2. Department of Electronics and Communication Engineering, Velammal Engineering College, Surapet, Chennai, India

    R. Venkatesan

Authors
  1. S. Rajalakshmi
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  2. R. Venkatesan
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Corresponding author

Correspondence to S. Rajalakshmi .

Editor information

Editors and Affiliations

  1. School of CS/IT, Indian Institute of Information Technology and Management, Trivandrum, Kerala, India

    Sabu M. Thampi

  2. Department of Electrical and Computer Engineering, Ryerson University, Toronto, Ontario, Canada

    Sri Krishnan

  3. Department of Computer Science, University of Salamanca, Salamanca, Salamanca, Spain

    Juan Manuel Corchado Rodriguez

  4. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, West Bengal, India

    Swagatam Das

  5. Department of Systems and Computer Networks, Wroclaw University of Science and Technology, Wroclaw, Poland

    Michal Wozniak

  6. Faculty of Engineering and Technology, Liverpool John Moores University, Liverpool, United Kingdom

    Dhiya Al-Jumeily

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Rajalakshmi, S., Venkatesan, R. (2018). Exploring Cepstral Coefficient Based Sleep Stage Scoring Method for Single-Channel EEG Signal Using Machine Learning Technique. In: Thampi, S., Krishnan, S., Corchado Rodriguez, J., Das, S., Wozniak, M., Al-Jumeily, D. (eds) Advances in Signal Processing and Intelligent Recognition Systems. SIRS 2017. Advances in Intelligent Systems and Computing, vol 678. Springer, Cham. https://doi.org/10.1007/978-3-319-67934-1_3

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  • DOI: https://doi.org/10.1007/978-3-319-67934-1_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67933-4

  • Online ISBN: 978-3-319-67934-1

  • eBook Packages: EngineeringEngineering (R0)

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