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Rough Set-Based Classification of EEG-Signals to Detect Intraoperative Awareness: Comparison of Fuzzy and Crisp Discretization of Real Value Attributes

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Rough Sets and Current Trends in Computing (RSCTC 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3066))

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Abstract

Automated classification of calculated EEG parameters has been shown to be a promising method for detection of intraoperative awareness. In the present study, rough set-based methods were employed to generate classification rules. For these methods, discrete attributes are required. We compared a crisp and a fuzzy discretization of the real parameter values. Fuzzy discretization transforms one real attribute value to several discrete values. By combining the different (discrete) values of all attributes, several sub-objects were produced from a single original object. Rule generation from a training set of objects and classification of a test set provided good classification rates of approximately 90% for both crisp and fuzzy discretization. Fuzzy discretization resulted in a simpler and smaller rule set than crisp discretization. Therefore, the simplicity of the resulting classifier justifies the higher computational effort caused by fuzzy discretization.

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Authors and Affiliations

  1. Department of Anesthesiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Straße 22, D-81675, München, Germany

    Michael Ningler, Gerhard Schneider, Oliver Dressler & Eberhard F. Kochs

  2. Institute of Information Technology, University Duisburg Essen, Bismarckstr. 90, D-47057, Duisburg, Germany

    Gudrun Stockmanns

Authors
  1. Michael Ningler
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  2. Gudrun Stockmanns
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  3. Gerhard Schneider
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  4. Oliver Dressler
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  5. Eberhard F. Kochs
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Editor information

Editors and Affiliations

  1. Shimane University, 89-1 Enya-cho Izumo, 6938501, Shimane, Japan

    Shusaku Tsumoto

  2. Systems Research Institute, Polish Academy of Sciences, 01-447, Warsaw, Poland

    Roman Słowiński

  3. The Linnaeus Centre for Bioinformatics, Uppsala University, Uppsala, Sweden

    Jan Komorowski

  4. Institute of Computer Science, Polish Academy of Sciences, 01–237, Warsaw, Poland

    Jerzy W. Grzymała-Busse

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© 2004 Springer-Verlag Berlin Heidelberg

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Ningler, M., Stockmanns, G., Schneider, G., Dressler, O., Kochs, E.F. (2004). Rough Set-Based Classification of EEG-Signals to Detect Intraoperative Awareness: Comparison of Fuzzy and Crisp Discretization of Real Value Attributes. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds) Rough Sets and Current Trends in Computing. RSCTC 2004. Lecture Notes in Computer Science(), vol 3066. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25929-9_105

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  • DOI: https://doi.org/10.1007/978-3-540-25929-9_105

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22117-3

  • Online ISBN: 978-3-540-25929-9

  • eBook Packages: Springer Book Archive

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