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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Automatic identification of epileptic EEG signals through binary magnetic optimization algorithms

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Pereira, Luis A. M. [1] ; Papa, Joao P. [2] ; Coelho, Andre L. V. [3] ; Lima, Clodoaldo A. M. [4] ; Pereira, Danillo R. [2] ; de Albuquerque, Victor Hugo C. [3]
Total Authors: 6
[1] Univ Estadual Campinas, Inst Comp, Campinas, SP - Brazil
[2] UNESP Univ Estadual Paulista, Dept Comp, Bauru, SP - Brazil
[3] Univ Fortaleza, Programa Posgrad Informat Aplicada, Fortaleza, CE - Brazil
[4] Univ Sao Paulo, Escola Artes Ciencias & Humanidades, Sao Paulo, SP - Brazil
Total Affiliations: 4
Document type: Journal article
Source: NEURAL COMPUTING & APPLICATIONS; v. 31, n. 2, p. 1317-1329, FEB 2019.
Web of Science Citations: 0

Epilepsy is a class of chronic neurological disorders characterized by transient and unexpected electrical disturbances of the brain. The automated analysis of the electroencephalogram (EEG) signal can be instrumental for the proper diagnosis of this mental condition. This work presents a systematic assessment of the performance of different variants of the binary magnetic optimization algorithm (BMOA), two of which are introduced here, while serving as feature selectors for epileptic EEG signal identification. In this context, the optimum-path forest classifier was adopted as a classification model, whereas different wavelet families were considered for EEG feature extraction. In order to compare the performance of the improved BMOA variants against the traditional one, as well as other metaheuristic techniques, namely particle swarm optimization, binary bat algorithm, and genetic algorithm, we employed a well-known EEG benchmark dataset composed of five classes of EEG signals (two of which comprising normal patients with eyes open or closed, and the remaining comprising ill patients with different levels of epilepsy). Overall, the results evidenced the robustness of the proposed BMOA and its variants. (AU)

FAPESP's process: 14/16250-9 - On the parameter optimization in machine learning techniques: advances and paradigms
Grantee:João Paulo Papa
Support type: Regular Research Grants
FAPESP's process: 09/16206-1 - New trends on optimum-path forest-based pattern recognition
Grantee:João Paulo Papa
Support type: Research Grants - Young Investigators Grants
FAPESP's process: 11/14094-1 - Exploring Multi-labeling approaches by Optimum-Path Forest
Grantee:Luis Augusto Martins Pereira
Support type: Scholarships in Brazil - Master