Publicação
Parametric Models for Characterization, Quantification and Defection of Epileptform Events in the Electroencephalogram
| Resumo: | This work presents an automated method based on the autoregressive (AR) modelling of the electroencephalogram (EEG). The EEG signal is divided in short segments (typically 2 seconds) and AR models subsequently evaluated. The model parameters quantify each segment and constitute features for classification using pattern recognition techniques. The method was validated with a data set including three types of epilepyic signals: petit mal (3 hours and 45 minutes; 7 patient), interictal spikes (10 minutes; 1 patient) and partial complex seizures (2 hours; 2 patients). (...) |
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| Autores principais: | Vaz, Francisco António Cardoso |
| Outros Autores: | Príncipe, José Carlos |
| Assunto: | EEG Epilepsia Modelos Autoregressivos Processamento digital de sinal EEG Epilepsy Autoregressive modelling Digital signal processing |
| Ano: | 1999 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Instituição associada: | Universidade de Aveiro Departamento de Electrónica Telecomunicações e Informática |
| Idioma: | português |
| Origem: | Electrónica e Telecomunicações |
| Resumo: | This work presents an automated method based on the autoregressive (AR) modelling of the electroencephalogram (EEG). The EEG signal is divided in short segments (typically 2 seconds) and AR models subsequently evaluated. The model parameters quantify each segment and constitute features for classification using pattern recognition techniques. The method was validated with a data set including three types of epilepyic signals: petit mal (3 hours and 45 minutes; 7 patient), interictal spikes (10 minutes; 1 patient) and partial complex seizures (2 hours; 2 patients). (...) |
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