Abstract
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.
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Duque-Muñoz, L., Pinzon-Morales, R.D., Castellanos-Dominguez, G. (2015). EEG Rhythm Extraction Based on Relevance Analysis and Customized Wavelet Transform. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo-Moreo, F., Adeli, H. (eds) Artificial Computation in Biology and Medicine. IWINAC 2015. Lecture Notes in Computer Science(), vol 9107. Springer, Cham. https://doi.org/10.1007/978-3-319-18914-7_44
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DOI: https://doi.org/10.1007/978-3-319-18914-7_44
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18913-0
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