  A variety of screening approaches have been proposed to diagnose epileptic
seizures, using electroencephalography (EEG) and magnetic resonance imaging
(MRI) modalities. Artificial intelligence encompasses a variety of areas, and
one of its branches is deep learning (DL). Before the rise of DL, conventional
machine learning algorithms involving feature extraction were performed. This
limited their performance to the ability of those handcrafting the features.
However, in DL, the extraction of features and classification are entirely
automated. The advent of these techniques in many areas of medicine, such as in
the diagnosis of epileptic seizures, has made significant advances. In this
study, a comprehensive overview of works focused on automated epileptic seizure
detection using DL techniques and neuroimaging modalities is presented. Various
methods proposed to diagnose epileptic seizures automatically using EEG and MRI
modalities are described. In addition, rehabilitation systems developed for
epileptic seizures using DL have been analyzed, and a summary is provided. The
rehabilitation tools include cloud computing techniques and hardware required
for implementation of DL algorithms. The important challenges in accurate
detection of automated epileptic seizures using DL with EEG and MRI modalities
are discussed. The advantages and limitations in employing DL-based techniques
for epileptic seizures diagnosis are presented. Finally, the most promising DL
models proposed and possible future works on automated epileptic seizure
detection are delineated.
