Epileptic seizures detection using deep learning techniques: A review Shoeibi, A, Khodatars, M, Ghassemi, N, Jafari, M, Moridian, P, Alizadehsani, Roohallah, Panahiazar, M, Khozeimeh, Fahime, Zare, A, Hosseini-Nejad, H, Khosravi, Abbas, Atiya, AF, Aminshahidi, D, Hussain, S, Rouhani, M, Nahavandi, Saeid and Acharya, UR 2021, Epileptic seizures detection using deep learning techniques: A review, International Journal of Environmental Research and Public Health, vol. 18, no. 11, pp. 1-33, doi: 10.3390/ijerph18115780. Attached Files Name Description MIMEType Size Downloads Title Epileptic seizures detection using deep learning techniques: A review Author(s) Shoeibi, A Khodatars, M Ghassemi, N Jafari, M Moridian, P Alizadehsani, Roohallah Panahiazar, M Khozeimeh, Fahime Zare, A orcid.org/0000-0001-6927-0744 Hosseini-Nejad, H Khosravi, Abbas Atiya, AF Aminshahidi, D Hussain, S orcid.org/0000-0002-0360-5270 Rouhani, M Nahavandi, Saeid Acharya, UR Journal name International Journal of Environmental Research and Public Health Volume number 18 Issue number 11 Article ID 5780 Start page 1 End page 33 Total pages 33 Publisher MDPI AG Place of publication Basel, Switzerland Publication date 2021-05-27 ISSN 1661-7827 1660-4601 Keyword(s) classification deep learning diagnosis EEG epileptic seizures feature extraction MRI Science & Technology Life Sciences & Biomedicine Environmental Sciences Public, Environmental & Occupational Health Environmental Sciences & Ecology NEURAL-NETWORK WAVELET TRANSFORM BIG DATA SIGNAL PREDICTION ELECTROENCEPHALOGRAM REPRESENTATION COMPLEXITY FILTERS Summary 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. Language eng DOI 10.3390/ijerph18115780 HERDC Research category C1 Refereed article in a scholarly journal Persistent URL http://hdl.handle.net/10536/DRO/DU:30152158 Document type: Journal Article Collection: Institute for Intelligent Systems Research and Innovation (IISRI) Related Links Link Description Connect to published version Go to link with your DU access privileges   Author URL Go to link with your DU access privileges   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 07 Jun 2021, 08:24:47 EST Tue, 08 Jun 2021, 05:01:56 EST Tue, 08 Jun 2021, 08:07:14 EST Sat, 26 Jun 2021, 02:31:43 EST Thu, 30 Jun 2022, 12:15:24 EST Thu, 30 Jun 2022, 16:15:43 EST Thu, 30 Jun 2022, 16:40:24 EST Tue, 02 Aug 2022, 12:48:50 EST Filtered Full Citation counts:   Cited 37 times in TR Web of Science Cited 49 times in Scopus Search Google Scholar Access Statistics: 5 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Mon, 07 Jun 2021, 08:24:47 EST