Classification of electroencephalogram signals using wavelet transform and particle swarm optimization
作者： Nasser Omer Ba-Karait , Siti Mariyam Shamsuddin , Rubita Sudirman
DOI: 10.1007/978-3-319-11897-0_41
关键词:
摘要: The electroencephalogram (EEG) is a signal measuring activities of the brain. Therefore, it contains useful information for diagnosis epilepsy. However, very time consuming and costly task to handle these subtle details by human observer. In this paper, particle swarm optimization (PSO) was proposed automate process seizure detection in EEG signals. Initially, signals have been analysed using discrete wavelet transform (DWT) features extraction. Then, PSO algorithm has trained recognize epileptic data. results demonstrate effectiveness method terms classification accuracy stability. A comparison with other methods literature confirms superiority PSO.
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