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...

Full description

Saved in:
Bibliographic Details
Published in:Neural computing & applications Vol. 31; no. Suppl 2; pp. 1317 - 1329
Main Authors: 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
Language:English
Published: London Springer London 13.02.2019
Springer Nature B.V
Subjects:
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 Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-017-3124-3