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

  • Iosif Mporas
  • Anastasia Efstathiou
  • Vasileios Megalooikonomou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Sleep stages Electroencephalography Clustering 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Iosif Mporas
    • 1
  • Anastasia Efstathiou
    • 1
  • Vasileios Megalooikonomou
    • 1
  1. 1.Multidimensional Data Analysis and Knowledge Management Laboratory, Department of Computer Engineering and InformaticsUniversity of PatrasRion-PatrasGreece

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