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International Conference on Brain Informatics and Health

BIH 2015: Brain Informatics and Health pp 77–85Cite as

Sleep Stages Classification from Electroencephalographic Signals Based on Unsupervised Feature Space Clustering

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9250))

Abstract

In this article we present a methodology for the automatic classification of sleep stages. The methodology relies on short-time analysis with time and frequency domain features followed by unsupervised feature subspace clustering. For each cluster of the feature space a different classification setup is adopted thus fine-tuning the classification algorithm to the specifics of the corresponding feature subspace area. The experimental results showed that the proposed methodology achieved a sleep stage classification accuracy equal to 92.53%, which corresponds to an improvement of approximately 3% compared to the best performing single classifier without applying clustering of the feature space.

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Author information

Authors and Affiliations

  1. Multidimensional Data Analysis and Knowledge Management Laboratory, Department of Computer Engineering and Informatics, University of Patras, 26500, Rion-Patras, Greece

    Iosif Mporas, Anastasia Efstathiou & Vasileios Megalooikonomou

Authors
  1. Iosif Mporas
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  2. Anastasia Efstathiou
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  3. Vasileios Megalooikonomou
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Corresponding author

Correspondence to Iosif Mporas .

Editor information

Editors and Affiliations

  1. Imperial College London , Data Science Institute, London, United Kingdom

    Yike Guo

  2. Institute of Cognitive Neuroscience, University College London, London, United Kingdom

    Karl Friston

  3. Imperial College London, London, United Kingdom

    Faisal Aldo

  4. Campus Biotech, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland

    Sean Hill

  5. Allen Institute for Brain Science, Seattle, USA

    Hanchuan Peng

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Mporas, I., Efstathiou, A., Megalooikonomou, V. (2015). Sleep Stages Classification from Electroencephalographic Signals Based on Unsupervised Feature Space Clustering. In: Guo, Y., Friston, K., Aldo, F., Hill, S., Peng, H. (eds) Brain Informatics and Health. BIH 2015. Lecture Notes in Computer Science(), vol 9250. Springer, Cham. https://doi.org/10.1007/978-3-319-23344-4_8

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  • DOI: https://doi.org/10.1007/978-3-319-23344-4_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23343-7

  • Online ISBN: 978-3-319-23344-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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