Epileptic Seizures Detection Using Deep Learning Techniques: A Review

By A. Shoeibi, Marjane Khodatars, Navid Ghassemi, M. Jafari, Parisa Moridian, Roohallah Alizadehsani, Maryam Panahiazar, F. Khozeimeh, A. Zare, Hossein Hosseini-Nejad and 7 more. International Journal of Environmental Research and Public Health, 2020

385 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Psychiatry and Mental health

View Paper: https://www.semanticscholar.org/paper/52ec59c8579339d5f9637c54b4d36ffd95808912

Citations in the corpus

• Near-Real-Time Epileptic Seizure Detection with Reduced EEG Electrodes: A BiLSTM-Wavelet Approach on the EPILEPSIAE Dataset (2026, unknown): “Deep learning techniques have significantly advanced the field of automated EEG-based seizure detection by enabling models to learn complex spatial and temporal patterns directly from data [11].”