Automatic identification of epileptic EEG signals through binary magnetic optimization algorithms

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...

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Vydáno v:	Neural computing & applications Ročník 31; číslo Suppl 2; s. 1317 - 1329
Hlavní autoři:	Pereira, Luís A. M., Papa, João P., Coelho, André L. V., Lima, Clodoaldo A. M., Pereira, Danillo R., de Albuquerque, Victor Hugo C.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Springer London 13.02.2019 Springer Nature B.V
Témata:	Algorithms Artificial Intelligence Automation Brain Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Electroencephalography Epilepsy Feature extraction Genetic algorithms Heuristic methods Image Processing and Computer Vision Neurological diseases Original Article Particle swarm optimization Patients Probability and Statistics in Computer Science Selectors Wavelet analysis Feature selection Optimum-path forest Metaheuristics Epilepsy EEG signal classification Magnetic optimization algorithm
ISSN:	0941-0643, 1433-3058
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Abstract	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.
AbstractList	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.
Author	Lima, Clodoaldo A. M. de Albuquerque, Victor Hugo C. Papa, João P. Coelho, André L. V. Pereira, Danillo R. Pereira, Luís A. M.
Author_xml	– sequence: 1 givenname: Luís A. M. surname: Pereira fullname: Pereira, Luís A. M. organization: Instituto de Computação, Universidade Estadual de Campinas – sequence: 2 givenname: João P. orcidid: 0000-0002-6494-7514 surname: Papa fullname: Papa, João P. email: papa@fc.unesp.br organization: Departamento de Computação, UNESP - Univ Estadual Paulista – sequence: 3 givenname: André L. V. surname: Coelho fullname: Coelho, André L. V. organization: Programa de Pós-Graduação em Informática Aplicada, Universidade de Fortaleza – sequence: 4 givenname: Clodoaldo A. M. surname: Lima fullname: Lima, Clodoaldo A. M. organization: Escola de Artes, Ciências e Humanidades, Universidade de São Paulo – sequence: 5 givenname: Danillo R. surname: Pereira fullname: Pereira, Danillo R. organization: Departamento de Computação, UNESP - Univ Estadual Paulista – sequence: 6 givenname: Victor Hugo C. surname: de Albuquerque fullname: de Albuquerque, Victor Hugo C. organization: Programa de Pós-Graduação em Informática Aplicada, Universidade de Fortaleza
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Cites_doi	10.1002/ima.20188 10.1016/j.neucom.2011.04.029 10.1109/TITB.2009.2017939 10.1016/j.patcog.2011.07.013 10.1016/j.dsp.2008.07.004 10.1016/j.eswa.2007.02.006 10.1016/j.eswa.2006.02.005 10.1016/j.cmpb.2004.10.009 10.1007/978-3-540-35488-8 10.1016/j.sigpro.2008.01.026 10.1016/j.eswa.2007.08.088 10.1142/7324 10.1016/j.artmed.2011.07.003 10.1016/j.compbiomed.2010.06.005 10.1016/j.eswa.2013.09.023 10.1016/0013-4694(82)90038-4 10.1016/S0165-1684(97)00038-8 10.1016/j.eswa.2009.01.022 10.1016/j.compbiomed.2009.06.001 10.1007/978-1-4612-0001-7 10.1016/j.neucom.2014.01.020 10.1155/2007/80510 10.1016/j.patrec.2008.11.012 10.1007/s00707-009-0270-4 10.1016/j.eswa.2004.12.027 10.1056/NEJMra022308 10.1103/PhysRevE.64.061907 10.1016/S0920-1211(01)00195-4 10.1109/AIPR.2004.41 10.1109/CEC.2008.4631155 10.1109/IGARSS.2011.6049683 10.1109/SIBGRAPI.2012.47
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Snippet	Epilepsy is a class of chronic neurological disorders characterized by transient and unexpected electrical disturbances of the brain. The automated analysis of...
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SubjectTerms	Algorithms Artificial Intelligence Automation Brain Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Electroencephalography Epilepsy Feature extraction Genetic algorithms Heuristic methods Image Processing and Computer Vision Neurological diseases Original Article Particle swarm optimization Patients Probability and Statistics in Computer Science Selectors Wavelet analysis
Title	Automatic identification of epileptic EEG signals through binary magnetic optimization algorithms
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