INVESTIGADORES
RISK Marcelo Raul
artículos
Título:
Multivariate Bayesian classification of epilepsy EEG signals
Autor/es:
QUINTERO-RINCÓN, ANTONIO; PRENDES, JORGE; PEREYRA, MARCELO; BATATIA, HADJ; RISK, MARCELO
Revista:
2016 IEEE 12th Image, Video, and Multidimensional Signal Processing Workshop, IVMSP 2016
Editorial:
Institute of Electrical and Electronics Engineers Inc.
Referencias:
Año: 2016
Resumen:
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.
