


Volume 16 No 4 (2018)
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A Classification Method for Epileptic Electroencephalogram Based on Wavelet Multi-scale Analysis and Particle Swarm Optimization Algorithm
Yiming Tian, Wei Chen, Xitai Wang
Abstract
The automatic classification of epileptic electroencephalogram (EEG) is important for the diagnosis and treatment
of epilepsy. In this paper, an epileptic EEG classification method based on wavelet multi-scale analysis and particle
swarm optimization is proposed. Firstly, the multi-scale is carried out to the original EEG to extract its sub-bands of
different frequency. Secondly, the Hurst exponent and the sample entropy are used to extract the EEG signals and
its sub-bands. Finally, the particle swarm optimization (PSO) algorithm is used to optimize the parameters of the
extreme learning machine (ELM), and the obtained eigenvector is put to PSO-ELM to realize the purpose of
classification of epileptic EEG. The proposed method in this paper achieved 99.7% classification accuracy for the
discrimination between epileptic ictal and interictal EEG, which is superior to those methods in other studies.
Keywords
Epileptic Electroencephalogram, Particle Swarm Optimization, Wavelet Multi-scale Analysis, Extreme Learning Machine, Classification
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