Aayesha1, Muhammad Bilal Qureshi2, Muhammad Afzaal3, Muhammad Shuaib Qureshi4, Jeonghwan Gwak5,6,7,8,*
CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5601-5630, 2022, DOI:10.32604/cmc.2022.020348
Abstract Detection of epileptic seizures on the basis of Electroencephalogram (EEG) recordings is a challenging task due to the complex, non-stationary and non-linear nature of these biomedical signals. In the existing literature, a number of automatic epileptic seizure detection methods have been proposed that extract useful features from EEG segments and classify them using machine learning algorithms. Some characterizing features of epileptic and non-epileptic EEG signals overlap; therefore, it requires that analysis of signals must be performed from diverse perspectives. Few studies analyzed these signals in diverse domains to identify distinguishing characteristics of epileptic EEG signals. To pose the challenge mentioned… More >
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