Abstract
Epileptic seizure(ES) detectionis anactive research area, that aims at patient-specific ESdetection with high accuracy from electroencephalogram (EEG) signals. The early detection of seizure is crucial for timely medical intervention and preventionof further injuries of the patients. This work proposes a robust deep learning framework called HyEpiSeiD that extracts self-trained features from the pre-processed EEGsignals using a hybrid combination of convolutional neural network followed by two gated recurrent unit layers and performs prediction based on thoseextracted features. The proposed HyEpiSeiD framework is evaluated on two public datasets, the UCI Epilepsy and Mendeleydatasets. The proposed HyEpiSeiD model achieved 99.01% and 97.50% classification accuracy, respectively, outperforming most of the state-of-the-art methods in epilepsy detection domain.
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