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Finding predictive EEG complexity features for classification of epileptic and psychogenic nonepileptic seizures using imperialist competitive algorithm

Authors :
Ahmadi, N.
Carrette, Evelien
Aldenkamp, A.P.
Pechenizkiy, M.
Ahmadi, N.
Carrette, Evelien
Aldenkamp, A.P.
Pechenizkiy, M.
Source :
Proceedings - 31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018, p.164-169. Piscataway: Institute of Electrical and Electronics Engineers.
Publication Year :
2018

Abstract

In this study, the imperialist competitive algorithm (ICA) is applied for classification of epileptic seizure and psychogenic nonepileptic seizure (PNES). For this purpose, after decomposing the EEG signal into five sub-bands and extracting some complexity features of EEG, the ICA is applied to find the predictive feature subset that maximizes the classification performance in the frequency spectrum. Results show that the spectral entropy and Renyi entropy are the most important EEG features as they are always appeared in the best feature subsets when applying different classifiers. Also, it is observed that the SVM-RBF and SVM-linear models are the best classifiers resulting in highest performance metrics compared to other classifiers. Our study shows that the reported algorithm is able to classify the epileptic seizure and PNES with a very high classification metrics.

Details

Database :
OAIster
Journal :
Proceedings - 31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018, p.164-169. Piscataway: Institute of Electrical and Electronics Engineers.
Notes :
Ahmadi, N.
Publication Type :
Electronic Resource
Accession number :
edsoai.on1048593815
Document Type :
Electronic Resource