… seizure. In this work, Long Short-Term Memory (LSTM) networks are introduced in epileptic
seizure … the use of deep learning algorithms with convolutional neural networks (CNN). A pre-…

… In this study, the LSTM network and CNN were combined to create a spatiotemporal
deep learning model for predicting epileptic seizures. The process used to construct the …

… identification of epileptic episodes. In this paper, a deep convolutional network with eight
convolutional layers and two fully-connected layers is provided to learn the characteristics …

… In this study, a comprehensive overview of works focused on automated epileptic seizure …
[57] used the AlexNet network to diagnose focal epileptic seizures. This proposed network …

… this preictal state of a seizure but an effective prediction in terms … a seizure prediction system
that employs deep learning … ; using convolution neural network and classification with the …

… that specifically cover epilepsy seizure prediction using EEG … of ML methods for predicting
epileptic seizures. For instance, … deep learning network for the prediction of epileptic seizures …

… for the detection of epileptic seizures. However, many of … learning model is presented for
detection of epileptic seizure … neural network (CNN) and the concept of transfer learning. The …

… systems (CADS) based on deep learning (DL) and neuroimaging … epileptic seizures detection
and prediction using neuroimaging modalities. First, DL-based CADS for epileptic seizures …

… of the convolutional neural network in extracting … network in expecting the incidence of
seizures earlier than the current methods. A semi-supervised approach based on transfer learning …

… As one of the most common neurological disorders, epilepsy causes great physical and …
seizures make the prediction necessary. In this paper, a novel approach for epileptic seizure …