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

  • S. RajalakshmiEmail author
  • R. Venkatesan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 678)


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.


Cognitive tasks Discrete Wavelet Transform Mel Frequency Cepstral Coefficient Feature extraction Statistical properties Gaussian mixture model-expectation maximization 


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Electrical and Electronics EngineeringVelammal Engineering CollegeChennaiIndia
  2. 2.Department of Electronics and Communication EngineeringVelammal Engineering CollegeChennaiIndia

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