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Epileptic seizures detection using deep learning techniques: A review
journal contribution
posted on 2021-05-27, 00:00 authored by A Shoeibi, M Khodatars, N Ghassemi, M Jafari, P Moridian, Roohallah AlizadehsaniRoohallah Alizadehsani, M Panahiazar, Fahime KhozeimehFahime Khozeimeh, A Zare, H Hosseini-Nejad, Abbas KhosraviAbbas Khosravi, A F Atiya, D Aminshahidi, S Hussain, M Rouhani, Saeid Nahavandi, U R AcharyaA 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.
History
Journal
International Journal of Environmental Research and Public HealthVolume
18Issue
11Article number
5780Pagination
1 - 33Publisher
MDPI AGLocation
Basel, SwitzerlandPublisher DOI
ISSN
1661-7827eISSN
1660-4601Language
engPublication classification
C1 Refereed article in a scholarly journalUsage metrics
Categories
Keywords
classificationdeep learningdiagnosisEEGepileptic seizuresfeature extractionMRIScience & TechnologyLife Sciences & BiomedicineEnvironmental SciencesPublic, Environmental & Occupational HealthEnvironmental Sciences & EcologyNEURAL-NETWORKWAVELET TRANSFORMBIG DATASIGNALPREDICTIONELECTROENCEPHALOGRAMREPRESENTATIONCOMPLEXITYFILTERSSimulation and Modelling
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