Epileptic Seizures Detection Using Deep Learning Techniques: A Review

TL;DR

This review paper discusses how deep learning techniques have advanced automated detection of epileptic seizures using EEG and MRI, highlighting methods, challenges, and future directions in the field.

Contribution

It provides a comprehensive overview of deep learning applications in epileptic seizure detection and rehabilitation, comparing various models and identifying key challenges and future prospects.

Findings

Deep learning improves seizure detection accuracy.

Various DL models have been applied to EEG and MRI data.

Challenges include data quality and model interpretability.

Abstract

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.