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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Veröffentlicht in:	Neural computing & applications Jg. 31; H. Suppl 2; S. 1317 - 1329
Hauptverfasser:	Pereira, Luís A. M., Papa, João P., Coelho, André L. V., Lima, Clodoaldo A. M., Pereira, Danillo R., de Albuquerque, Victor Hugo C.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 13.02.2019 Springer Nature B.V
Schlagworte:	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
Online-Zugang:	Volltext
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