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Type: Artigo
Title: Electroencephalogram Signal Classification Based On Shearlet And Contourlet Transforms
Author: Amorim
Paulo; Moraes
Thiago; Fazanaro
Dalton; Silva
Jorge; Pedrini
Abstract: 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.
Subject: Epilepsy
Electroencephalogram Signals
Editor: Pergamon-Elsevier Science LTD
Rights: fechado
Identifier DOI: 10.1016/j.eswa.2016.09.037
Date Issue: 2017
Appears in Collections:Unicamp - Artigos e Outros Documentos

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