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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Electroencephalogram signal classification based on shearlet and contourlet transforms

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Autor(es):
Amorim, Paulo ; Moraes, Thiago ; Fazanaro, Dalton ; Silva, Jorge ; Pedrini, Helio
Número total de Autores: 5
Tipo de documento: Artigo Científico
Fonte: EXPERT SYSTEMS WITH APPLICATIONS; v. 67, p. 140-147, JAN 2017.
Citações Web of Science: 12
Resumo

Epilepsy is a disorder that affects approximately 50 million people of all ages, according to World Health Organization (2016), which makes it one of the most common neurological diseases worldwide. Electroencephalogram (EEG) signals have been widely used to detect epilepsy and other brain abnormalities. In this work, we propose and evaluate a novel methodology based on shearlet and contourlet transforms to decompose the EEG signals into frequency bands. A set of features are extracted from these time frequency coefficients and used as input to different classifiers. Experiments are conducted on a public data set to demonstrate the effectiveness of the proposed classification method. The developed system can help neurophysiologists identify EEG patterns in epilepsy diagnostic tasks. (C) 2016 Elsevier Ltd. All rights reserved. (AU)

Processo FAPESP: 11/22749-8 - Desafios em visualização exploratória de dados multidimensionais: novos paradigmas, escalabilidade e aplicações
Beneficiário:Luis Gustavo Nonato
Linha de fomento: Auxílio à Pesquisa - Temático