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Review
. 2021 May 27;18(11):5780.
doi: 10.3390/ijerph18115780.

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

Afshin Shoeibi  1   2 , Marjane Khodatars  3 , Navid Ghassemi  1   2 , Mahboobeh Jafari  4 , Parisa Moridian  5 , Roohallah Alizadehsani  6 , Maryam Panahiazar  7 , Fahime Khozeimeh  6 , Assef Zare  8 , Hossein Hosseini-Nejad  9 , Abbas Khosravi  6 , Amir F Atiya  10 , Diba Aminshahidi  2 , Sadiq Hussain  11 , Modjtaba Rouhani  2 , Saeid Nahavandi  6 , Udyavara Rajendra Acharya  12   13   14
Affiliations
Review

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

Afshin Shoeibi et al. Int J Environ Res Public Health. .

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.

Keywords: EEG; MRI; classification; deep learning; diagnosis; epileptic seizures; feature extraction.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Number of times each DL tool was used for automated detection of epileptic seizure by various studies.
Figure 2
Figure 2
Number of studies conducted using various DL models from 2014 until now (2021).
Figure 3
Figure 3
Search strategy used.
Figure 4
Figure 4
Block diagram of a DL-based CAD system for epileptic seizures.
Figure 5
Figure 5
Usage of various datasets for automated detection of seizure using DL techniques by various studies.
Figure 6
Figure 6
A typical 2D-CNN for epileptic seizure detection.
Figure 7
Figure 7
Sketch of accuracy (%) obtained by various authors using 2D-CNN models for seizure detection.
Figure 8
Figure 8
Typical sketch of the 1D-CNN model that can be used for epileptic seizure detection.
Figure 9
Figure 9
Sketch of accuracy (%) versus authors obtained using 1D-CNN models for seizure detection.
Figure 10
Figure 10
Sample RNN model that can be used for seizure detection.
Figure 11
Figure 11
Sketch of accuracy (%) obtained by authors using RNN models for seizure detection.
Figure 12
Figure 12
Sample AE network that may be used for seizure detection.
Figure 13
Figure 13
Sketch of accuracy (%) versus authors obtained using AE models for seizure detection.
Figure 14
Figure 14
Sketch of accuracy (%) versus different researchers obtained using CNN-RNN models for seizure detection.
Figure 15
Figure 15
Sketch of accuracy (%) versus authors obtained using CNN-AE models for seizure detection.
Figure 16
Figure 16
Block diagram of proposed epileptic seizure detection system using DL methods with EEG signals.
See this image and copyright information in PMC

References

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