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Investigation of Neonatal EEG Time Series Using a Modified Nonlinear Dynamical Analysis

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Neural Information Processing (ICONIP 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5864))

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Abstract

The Grassberger-Procaccia algorithm for computation of the correlation dimension is widely used nonlinear dynamical analysis techniques for EEG time series analysis. Even though the correlation dimension D 2 is the easiest dimension to compute, major drawback of the Grassberger-Procaccia algorithm is its extensive computational requirements. To overcome this, we introduce a modified computational algorithm referred to as the partial correlation integral. The partial correlation integral algorithm provides an approximation of the correlation dimension referred to as the dimensional exponent. Similar to the correlation dimension, the dimensional exponent can serve as a relative index of the complexity of a nonlinear dynamical system. In this study, the partial correlation integral algorithm is applied to analyze neonatal EEG sleep data. From the computational results, conclusions consistent with those made in previous studies using the correlation dimension are obtained.

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Author information

Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, Ubon Ratchathani University, Warinchamrab, Ubonratchathani, 34190, Thailand

    Suparerk Janjarasjitt

  2. Department of Neurology, Case Western Reserve University, Cleveland, Ohio, 44106, USA

    Mark S. Scher

  3. Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, Ohio, 44106, USA

    Suparerk Janjarasjitt & Kenneth A. Loparo

Authors
  1. Suparerk Janjarasjitt
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  2. Mark S. Scher
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  3. Kenneth A. Loparo
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Editor information

Editors and Affiliations

  1. Department of Electronic Engineering, City University of Hong Kong, Hong Kong,  

    Chi Sing Leung

  2. School of Electrical Engineering and Computer Science, Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, 702-701, Taegu, Korea

    Minho Lee

  3. School of Information Technology, King Mongkut’s University of Technology Thonburi, 126 Pracha-U-Thit Rd., Bangmod, Thungkru, 10140, Bangkok, Thailand

    Jonathan H. Chan

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© 2009 Springer-Verlag Berlin Heidelberg

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Janjarasjitt, S., Scher, M.S., Loparo, K.A. (2009). Investigation of Neonatal EEG Time Series Using a Modified Nonlinear Dynamical Analysis. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5864. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10684-2_36

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  • DOI: https://doi.org/10.1007/978-3-642-10684-2_36

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10682-8

  • Online ISBN: 978-3-642-10684-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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