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.
| Original language | English |
|---|---|
| Title of host publication | 2016 IEEE 12th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP) |
| Publisher | IEEE |
| ISBN (Electronic) | 9781509019298 |
| DOIs | |
| Publication status | Published - 4 Aug 2016 |
Keywords
- 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