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Sleep stage classification in EEG signals using the clustering approach based probability distribution features

Wessam Al-Salman1, Yan Li2, Atheer Y Oudah3

  • 1School of Mathematics, Physics and Computing, University of Southern Queensland, Australia; University of Thi-Qar, College of Education for Pure Science, Iraq.

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Summary

This study introduces an automated method for classifying sleep stages from EEG signals using clustering and probability distribution features. The novel approach achieves 97.4% accuracy, offering a valuable tool for diagnosing sleep disorders.

Keywords:
Discrete wavelet transformElectroencephalogram (EEG)Least squares support vector machine classifier and probability distributionSleep stages

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Manual sleep scoring from Electroencephalogram (EEG) signals is time-consuming and requires expert interpretation.
  • Accurate sleep stage classification is crucial for diagnosing sleep disorders.

Purpose of the Study:

  • To develop an automated method for classifying six sleep stages using EEG signals.
  • To overcome the limitations of manual sleep scoring.

Main Methods:

  • EEG signals were segmented and decomposed using discrete wavelet transform (DWT).
  • Wavelet coefficients were clustered using k-means algorithm.
  • Features were extracted based on probability distribution and classified using least squares support vector machine (LS-SVM).

Main Results:

  • The proposed automated method achieved an average accuracy rate of 97.4% for sleep stage classification.
  • The method demonstrated robust performance in identifying six distinct sleep stages.

Conclusions:

  • The developed method provides an effective and accurate tool for automated sleep stage classification.
  • This approach can assist physicians and neurologists in diagnosing sleep disorders more efficiently.