Multivariate Bayesian classification of epilepsy EEG signals

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 ener...

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Bibliographic Details
Published in2016 IEEE 12th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP) pp. 1 - 5
Main Authors Quintero-Rincon, Antonio, Prendes, Jorge, Pereyra, Marcelo, Batatia, Hadj, Risk, Marcelo
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2016
Subjects
Bayes methods
Bayesian classifiers
Brain modeling
EEG
Electroencephalography
Epilepsy
Gaussian distribution
Generalized Gaussian distribution
Multilevel 2D wavelet
Sensitivity
Two dimensional displays
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Summary: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.
DOI:10.1109/IVMSPW.2016.7528180