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.
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