Variational mode decomposition and binary grey wolf optimization-based automated epilepsy seizure classification framework.
Biomed Tech (Berl)
; 68(2): 147-163, 2023 Apr 25.
Article
en En
| MEDLINE
| ID: mdl-36583250
ABSTRACT
This work proposes a variational mode decomposition (VMD) and binary grey wolf optimization (BGWO) based seizure classification framework. VMD decomposes the EEG signal into band-limited intrinsic mode function (BL-IMFs) non-recursively. The frequency domain, time domain, and information theory-based features are extracted from the BL-IMFs. Further, an optimal feature subset is selected using BGWO. Finally, the selected features were utilized for classification using six different supervised machine learning algorithms. The proposed framework has been validated experimentally by 58 test cases from the CHB-MIT scalp EEG and the Bonn University database. The proposed framework performance is quantified by average sensitivity, specificity, and accuracy. The selected features, along with Bayesian regularized shallow neural networks (BR-SNNs), resulted in maximum accuracy of 99.53 and 99.64 for 1 and 2 s epochs, respectively, for database 1. The proposed framework has achieved 99.79 and 99.84 accuracy for 1 and 2 s epochs, respectively, for database 2.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Convulsiones
/
Epilepsia
Límite:
Humans
Idioma:
En
Revista:
Biomed Tech (Berl)
Año:
2023
Tipo del documento:
Article
País de afiliación:
India