EEG Channel-Selection Method for Epileptic-Seizure Classification Based on Multi-Objective Optimization.

ABSTRACT: We present a multi-objective optimization method for electroencephalographic (EEG) channel selection based on the non-dominated sorting genetic algorithm (NSGA) for epileptic-seizure classification. We tested the method on EEG data of 24 patients from the CHB-MIT public dataset. The procedure starts by decomposing the EEG data from each channel into different frequency bands using the empirical mode decomposition (EMD) or the discrete wavelet transform (DWT), and then for each sub-band four features are extracted; two energy values and two fractal dimension values. The obtained feature vectors are then iteratively tested for solving two unconstrained objectives by NSGA-II or NSGA-III; to maximize classification accuracy and to reduce the number of EEG channels required for epileptic seizur

SUBMITTER: Moctezuma LA 

PROVIDER: S-EPMC7312219 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature