Saeed Mohsen1,2,*, Sherif S. M. Ghoneim3, Mohammed S. Alzaidi3, Abdullah Alzahrani3, Ashraf Mohamed Ali Hassan4
CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5271-5286, 2023, DOI:10.32604/cmc.2023.038758
Abstract Classification of electroencephalogram (EEG) signals for humans can be achieved via artificial intelligence (AI) techniques. Especially, the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic regions. From this perspective, an automated AI technique with a digital processing method can be used to improve these signals. This paper proposes two classifiers: long short-term memory (LSTM) and support vector machine (SVM) for the classification of seizure and non-seizure EEG signals. These classifiers are applied to a public dataset, namely the University of Bonn, which consists of 2 classes –seizure and non-seizure. In addition, a fast… More >
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