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Detection of sleep stages in neonatal EEG records

  • Conference paper
  • First Online:
EMBEC & NBC 2017 (EMBEC 2017, NBC 2017)

Part of the book series: IFMBE Proceedings ((IFMBE,volume 65))

Abstract

The aim of this study is the detection of changes in sleep stages in EEG recordings in full-term and preterm newborns. We use a k-NN algorithm as a method of classification. The novelty of our approach lies in semi-automatic etalon (prototype) selection with combination of temporal analysis for sleep stages detection. The semi-automated etalon extraction includes the k-means algorithm for etalons suggestion and an expert-in-the-loop for verification of these etalons. The semi-automated approach improves significantly the time spent on the etalon selection (extraction) by the expert. The whole procedure of EEG signal processing consists of adaptive segmentation, feature extraction, semi-automatic etalon selection using k-means and expert-in-the-loop, classification using k-NN algorithm and temporal profile analysis that is able to detect the neonatal sleep stages for the full-term and even for the preterm neonates, which makes it a unique detection method.

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Authors and Affiliations

  1. Czech Technical University in Prague, Faculty of Biomedical Engineering, Nam. Sitna 3105, Kladno, Czech Republic

    Vladimir Krajca, Vaclava Piorecka, Hana Schaabova, Jan Strobl, Marek Piorecky, Lenka Lhotska & Karel Paul

  2. National Institute of Mental Health, Kladno, Czech Republic

    Vladimir Krajca, Vaclava Piorecka, Jan Strobl & Marek Piorecky

  3. Czech Technical University in Prague, Czech Institute of Informatics, Robotics, and Cybernetics, Prague, Czech Republic

    Lenka Lhotska

Authors
  1. Vladimir Krajca
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  2. Vaclava Piorecka
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  3. Hana Schaabova
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  4. Jan Strobl
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  5. Marek Piorecky
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  6. Lenka Lhotska
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  7. Karel Paul
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Corresponding author

Correspondence to Vladimir Krajca .

Editor information

Editors and Affiliations

  1. Tampere University of Technology, BioMediTech Institute and Faculty of Biomedical Science and Engineering, Tampere, Finland

    Hannu Eskola

  2. Tampere University of Technology, BioMediTech Institute and Faculty of Biomedical Science and Engineering, Tampere, Etelä-Suomi, Finland

    Outi Väisänen

  3. Tampere University of Technology, BioMediTech Institute and Faculty of Biomedical Science and Engineering, Tampere, Etelä-Suomi, Finland

    Jari Viik

  4. Tampere University of Technology, BioMediTech Institute and Faculty of Biomedical Science and Engineering, Tampere, Etelä-Suomi, Finland

    Jari Hyttinen

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Krajca, V. et al. (2018). Detection of sleep stages in neonatal EEG records. In: Eskola, H., Väisänen, O., Viik, J., Hyttinen, J. (eds) EMBEC & NBC 2017. EMBEC NBC 2017 2017. IFMBE Proceedings, vol 65. Springer, Singapore. https://doi.org/10.1007/978-981-10-5122-7_63

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  • DOI: https://doi.org/10.1007/978-981-10-5122-7_63

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-5121-0

  • Online ISBN: 978-981-10-5122-7

  • eBook Packages: EngineeringEngineering (R0)

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