Page 119 - 2024-Vol20-Issue2

Received: 12 September 2023 | Revised: 9 October 2023 | Accepted: 20 October 2023

DOI: 10.37917/ijeee.20.2.10 Vol. 20 | Issue 2 | December 2024

Open Access

Iraqi Journal for Electrical and Electronic Engineering

Review Article

Epileptic detection based on deep learning: A review

Ola M. Assim*, Ahlam F. Mahmood
Mosul University, Computer Engineering Department, Mosul, Iraq

Correspondance
*Ola M. Assim
Computer Engineering Department, College of Engineering,
Mosul University, Mosul, Iraq.
Email: ola.marwan@uomosul.edu.iq

Abstract
Epilepsy, a neurological disorder characterized by recurring seizures, necessitates early and precise detection for
effective management. Deep learning techniques have emerged as powerful tools for analyzing complex medical
data, specifically electroencephalogram (EEG) signals, advancing epileptic detection. This review comprehensively
presents cutting-edge methodologies in deep learning-based epileptic detection systems. Beginning with an overview of
epilepsy’s fundamental concepts and their implications for individuals and healthcare are present. This review then
delves into deep learning principles and their application in processing EEG signals. Diverse research papers to know
the architectures—convolutional neural networks, recurrent neural networks, and hybrid models—are investigated,
emphasizing their strengths and limitations in detecting epilepsy. Preprocessing techniques for improving EEG data
quality and reliability, such as noise reduction, artifact removal, and feature extraction, are discussed. Present
performance evaluation metrics in epileptic detection, such as accuracy, sensitivity, specificity, and area under the curve,
are provided. This review anticipates future directions by highlighting challenges such as dataset size and diversity,
model interpretability, and integration with clinical decision support systems. Finally, this review demonstrates how deep
learning can improve the precision, efficiency, and accessibility of early epileptic diagnosis. This advancement allows for
more timely interventions and personalized treatment plans, potentially revolutionizing epilepsy management.

Keywords
Epileptic seizures, Deep Learning, Electroencephalogram, Convolution Neural Networks, Detection.

I. INTRODUCTION detection from EEG signals. This paper comprehensively re-
views the latest advancements in epileptic seizure detection
Epilepsy, a neurological disorder affecting approximately 50 using deep learning methodologies. By harnessing the power
million people worldwide, is characterized by spontaneous of deep learning, researchers have made substantial progress
and recurrent seizures [1]. These unpredictable seizures can in enhancing the accuracy of seizure detection. The paper
profoundly affect an individual’s quality of life, impacting contributes to the ongoing efforts to advance the quality of
their daily activities, mobility, and social interactions. Timely epilepsy management and the lives of individuals affected by
and accurate detection of seizures is paramount for managing this condition. The main contributions to this review are:
the disorder and enhancing patients’ overall well-being. Elec-
troencephalogram (EEG), a non-invasive technique used to 1. Comprehensive Review: This review provides a com-
monitor brain activity, is pivotal in diagnosing and managing prehensive overview of the current state of epileptic
epilepsy. However, interpreting EEG signals poses a signif- detection methodologies, explicitly focusing on deep
icant challenge due to their inherent complexity and noise. learning techniques applied to EEG signals. Cover-
This complexity often hinders the precise detection of seizures. ing fundamental concepts and their implications offers
In recent years, the emergence of deep learning techniques has readers a holistic understanding of the topic.
offered a promising avenue for improving epileptic seizure
2. Deep Learning Principles: present various deep learn-

This is an open-access article under the terms of the Creative Commons Attribution License,
which permits use, distribution, and reproduction in any medium, provided the original work is properly cited.
©2024 The Authors.
Published by Iraqi Journal for Electrical and Electronic Engineering | College of Engineering, University of Basrah.

https://doi.org/10.37917/ijeee.20.2.10 |https://www.ijeee.edu.iq 115