Multivariate Bayesian classification of epilepsy EEG signals

Antonio Quintero-Rincón, Jorge Prendes, Marcelo Pereyra, Hadj Batatia, Marcelo Risk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Citations (Scopus)


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.
Original languageEnglish
Title of host publication2016 IEEE 12th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP)
ISBN (Electronic)9781509019298
Publication statusPublished - 4 Aug 2016


  • Bayes methods
  • Gaussian processes
  • electroencephalography
  • medical signal processing
  • paediatrics
  • signal classification
  • wavelet transforms
  • biomedical engineering
  • brain rhythm
  • epilepsy EEG signals
  • epileptic seizure event classification
  • generalised Gaussian statistical representation
  • multilevel 2D wavelet decomposition
  • multivariate Bayesian classification
  • multivariate EEG signal
  • normal brain function signal
  • paediatric dataset
  • Brain modeling
  • Electroencephalography
  • Epilepsy
  • Gaussian distribution
  • Sensitivity
  • Two dimensional displays
  • Bayesian classifiers
  • EEG
  • Generalized Gaussian distribution
  • Multilevel 2D wavelet


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