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Multivariate Bayesian classification of epilepsy EEG signals

Abstract : The classification of epileptic seizure events in EEG signals is an important problem in biomedical engineering. In this paper we propose a Bayesian classification method for multivariate EEG signals. The method is based on a multilevel 2D wavelet decomposition that captures the distribution of energy across the different brain rhythms and regions, coupled with a generalised Gaussian statistical representation and a multivariate Bayesian classification scheme. The proposed approach is demonstrated on a challenging paediatric dataset containing both epileptic events and normal brain function signals, where it outperforms a state-of-the-art method both in terms of classification sensitivity and specificity.
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Submitted on : Wednesday, May 2, 2018 - 9:52:37 AM
Last modification on : Saturday, June 20, 2020 - 3:37:55 AM
Long-term archiving on: : Tuesday, September 25, 2018 - 7:08:44 PM


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  • HAL Id : hal-01782569, version 1
  • OATAO : 18949


Antonio Quintero Rincon, Jorge Prendes, Marcelo Alejandro Pereyra, Hadj Batatia, Marcelo Risk. Multivariate Bayesian classification of epilepsy EEG signals. 12th IEEE Workshop on Image, Video, and Multidimensional Signal Processing (IVMSP 2016), Jul 2016, Bordeaux, France. pp. 1-5. ⟨hal-01782569⟩



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