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Separation of Rhythms of EEG Signals Based on Hilbert-Huang Transformation with Application to Seizure Detection

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Convergence and Hybrid Information Technology (ICHIT 2012)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7425))

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Abstract

We present a new method for separation of the rhythms of the electroencephalogram (EEG) signal. The proposed method is based on the Hilbert-Huang transform (HHT). The HHT consists two steps namely empirical mode decomposition (EMD) and the Hilbert transform (HT). The EMD decomposes EEG signal into set of narrow-band intrinsic mode functions (IMFs), and the Hilbert transformation of these IMFs provide instantaneous frequency estimation of the IMFs. The instantaneous frequency estimation of IMFs have been used as a feature to identify the IMFs in order to separate rhythms of EEG signal. The central tendency measure (CTM) has been used to quantify the variability in second order difference (SOD) plots of rhythms of the EEG signal. The CTM parameter is very effective to discriminate epileptic seizure EEG signals from the seizure-free EEG signals. The experimental results show the effectiveness of the proposed method for epileptic seizure detection.

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Authors and Affiliations

  1. School of Engineering, Indian Institute of Technology Indore, 452017, Indore, India

    Varun Bajaj & Ram Bilas Pachori

Authors
  1. Varun Bajaj
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  2. Ram Bilas Pachori
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Editor information

Editors and Affiliations

  1. Dept of Computer Engineering, Hannam University, Korea

    Geuk Lee

  2. Computer Science and Information System, University of Limerick, Limerick, Ireland

    Daniel Howard

  3. Department of Information and Communication, Dong Seoul University, 423 Bokjeong-Dong, Sujeong-Gu, Seongnam, Gyunggi, Korea

    Jeong Jin Kang

  4. Institute of Mathematics, University of Warsaw, ul. Banacha 2, 02-097, Warsaw, Poland

    Dominik Ślęzak

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© 2012 Springer-Verlag Berlin Heidelberg

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Bajaj, V., Pachori, R.B. (2012). Separation of Rhythms of EEG Signals Based on Hilbert-Huang Transformation with Application to Seizure Detection. In: Lee, G., Howard, D., Kang, J.J., Ślęzak, D. (eds) Convergence and Hybrid Information Technology. ICHIT 2012. Lecture Notes in Computer Science, vol 7425. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32645-5_62

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  • DOI: https://doi.org/10.1007/978-3-642-32645-5_62

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32644-8

  • Online ISBN: 978-3-642-32645-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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