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

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9250)


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.


Sleep stages Electroencephalography Clustering 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Multidimensional Data Analysis and Knowledge Management Laboratory, Department of Computer Engineering and InformaticsUniversity of PatrasRion-PatrasGreece

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