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Epileptic Seizures Detection Using Deep Learning Techniques: A Review

Author

Listed:
  • Afshin Shoeibi

    (Faculty of Electrical Engineering, Biomedical Data Acquisition Lab (BDAL), K. N. Toosi University of Technology, Tehran 1631714191, Iran
    Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran)

  • Marjane Khodatars

    (Mashhad Branch, Islamic Azad University, Mashhad 91735413, Iran)

  • Navid Ghassemi

    (Faculty of Electrical Engineering, Biomedical Data Acquisition Lab (BDAL), K. N. Toosi University of Technology, Tehran 1631714191, Iran
    Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran)

  • Mahboobeh Jafari

    (Electrical and Computer Engineering Faculty, Semnan University, Semnan 3513119111, Iran)

  • Parisa Moridian

    (Faculty of Engineering, Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran)

  • Roohallah Alizadehsani

    (Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia)

  • Maryam Panahiazar

    (Institute for Computational Health Sciences, School of Medicine, University of California, San Francisco, CA 94143, USA)

  • Fahime Khozeimeh

    (Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia)

  • Assef Zare

    (Faculty of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad 6518115743, Iran)

  • Hossein Hosseini-Nejad

    (Faculty of Electrical and Computer Engineering, K. N. Toosi University of Technology, Tehran 1631714191, Iran)

  • Abbas Khosravi

    (Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia)

  • Amir F. Atiya

    (Department of Computer Engineering, Faculty of Engineering, Cairo University, Cairo 12613, Egypt)

  • Diba Aminshahidi

    (Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran)

  • Sadiq Hussain

    (System Administrator at Dibrugarh University, Assam 786004, India)

  • Modjtaba Rouhani

    (Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran)

  • Saeid Nahavandi

    (Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia)

  • Udyavara Rajendra Acharya

    (Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore
    Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
    Department of Bioinformatics and Medical Engineering, Taichung City 41354, Taiwan)

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.

Suggested Citation

  • Afshin Shoeibi & Marjane Khodatars & Navid Ghassemi & Mahboobeh Jafari & Parisa Moridian & Roohallah Alizadehsani & Maryam Panahiazar & Fahime Khozeimeh & Assef Zare & Hossein Hosseini-Nejad & Abbas K, 2021. "Epileptic Seizures Detection Using Deep Learning Techniques: A Review," IJERPH, MDPI, vol. 18(11), pages 1-33, May.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:11:p:5780-:d:563819
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    References listed on IDEAS

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    Cited by:

    1. Christian Riccio & Angelo Martone & Gaetano Zazzaro & Luigi Pavone, 2024. "Training Datasets for Epilepsy Analysis: Preprocessing and Feature Extraction from Electroencephalography Time Series," Data, MDPI, vol. 9(5), pages 1-10, April.

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