EEG Rhythm Extraction Based on Relevance Analysis and Customized Wavelet Transform

  • L. Duque-Muñoz
  • R. D. Pinzon-Morales
  • G. Castellanos-Dominguez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9107)


The waveform of physiological signals carries useful information about the brain states. Automated computational algorithms are used in clinical medicine for extracting this information that cannot be read directly by visual inspection. Nonetheless, difficulties arise in the extraction because the intrinsic rhythms of the waveforms vary with the changes in the state of the brain. That is the case for electroencephalogram (EEG) signals from Epileptic seizure events. Here, we address the extraction of information from EEG signals by using a novel methodology that quantitatively measures the intrinsic rhythms of EEG waveforms related to healthy or Epileptic seizure events. In this method, the customized wavelet is used to estimate the EEG rhythms and then the relevance analysis with Fuzzy entropy and Stochastic measure are used to discriminate between seizure free and seizure states. The classification stage is based on classification performance using a support vector machine classifier. The pertinence of the proposed methodology during the Epileptic seizure identification is discussed, and future directions are presented.


Customized wavelet Epileptic seizure detection Relevant analysis Brain state classification 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • L. Duque-Muñoz
    • 1
  • R. D. Pinzon-Morales
    • 2
  • G. Castellanos-Dominguez
    • 3
  1. 1.Research group SISTEMICUniversidad de AntioquiaMedellinColombia
  2. 2.The Neural Cybernetics laboratoryChubu UniversityKasugaiJaban
  3. 3.Signal Processing and Recognition GroupUniversidad Nacional de ColombiaBogotáColombia

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