| Full text | |
| Author(s): |
Amorim, Paulo
;
Moraes, Thiago
;
Fazanaro, Dalton
;
Silva, Jorge
;
Pedrini, Helio
Total Authors: 5
|
| Document type: | Journal article |
| Source: | EXPERT SYSTEMS WITH APPLICATIONS; v. 67, p. 140-147, JAN 2017. |
| Web of Science Citations: | 11 |
| 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. (AU) | |
| FAPESP's process: | 11/22749-8 - Challenges in exploratory visualization of multidimensional data: paradigms, scalability and applications |
| Grantee: | Luis Gustavo Nonato |
| Support Opportunities: | Research Projects - Thematic Grants |