Detection of Neurological Disorder Epileptic Seizures Using Various Approaches: A Review
Keywords:
Neurological Disorder, Epilepsy, Seizure Detection, Machine Learning, Deep LearningAbstract
Epilepsy is a serious, persistent neurological disorder that may be detected via brain signals generated by brain neurons. The abnormalities in the brain that cause epileptic seizures can have a negative impact on a patient's health. It appears all of sudden and shows no symptoms, that further increases the death rate of people. Nearly 1% of people worldwide experience epileptic seizures. Over the years, a number of techniques have been researched, put forth, and created. For the purpose of signal transmission and internal organ communication, neurons are closely coupled to one another. Electrocorticography (ECoG) and electroencephalography (EEG) media are typically accustomed to discover these brain impulses. These signals generate a large amount of data and are complicated, noisy, non-linear, and non-stationary. These restrictions on automated interictal spike and epileptic seizure identification, a crucial tool for closely reviewing and analyzing the EEG data, are recommended. These limitations draw our attention to a study of automated methods that may classify signals into epileptic and nonepileptic categories for neurologists. This paper presents a review on the primary difficulties that are observed during the implementation of epilepsy prediction algorithms, the paper also provides various feature selection and classification techniques.
Downloads
References
https://www.who.int/news-room/fact-sheets/detail/epilepsy
https://epilepsysociety.org.uk/about-epilepsy
https://www.aans.org/Patients/Neurosurgical-Conditions-and-Treatments/Epilepsy
K. Rasheed et al., “Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review"; IEEE Reviews in Biomedical Engineering, Volume. 14, pp. 139-155, 2021
Marzieh Savadkoohi, Timothy Oladunni, Lara Thompson; “A machine learning approach to epileptic seizure prediction using Electroencephalogram (EEG) Signal”; Elsevier, Bio-cybernetics and Biomedical Engineering, Volume 40, pp. 1328-1341, 7th July 2020
Michele Lo Giudice et al.; “Permutation Entropy-Based Interpretability of Convolutional Neural Network Models for Interictal EEG Discrimination of Subjects with Epileptic Seizures vs. Psychogenic Non-Epileptic Seizures” Entropy, 9th January 2022
Andhale, Praveen and Patil, Dr.Varsha; “Machine Learning Approach for Predicting Epileptic Seizures using EEG Signals: A Review “; SSRN (Elsevier), Proceedings of the 3rd International Conference on Contents, Computing & Communication, 25th February 2022
Mengni Zhou, Cheng Tian, et al; “Epileptic Seizure Detection Based on EEG Signals and CNN”; Frontiers, Frontiers in Neuro informatics, 10th December 2018.
Muhammad Haseeb Aslam et al.; “Classification of EEG Signals for Prediction of Epileptic Seizures”; MDPI, Appl. Sci. Volume 12, 19th July 2022
M. F. Pinto, A. Leal, F. Lopes, A. Dourado, P. Martins, and C. A. Teixeira, “A personalized and evolutionary algorithm for interpretable EEG epilepsy seizure prediction,” Scientific Reports, vol. 11, p. 3415, 2021.
F. George, A. Joseph, B. Baby et al., “Epileptic seizure prediction using EEG images,” in 2020 International Conference on Communication and Signal Processing (ICCSP), pp. 1595–1598, Chennai, India, 2020.
M. Dedeo and M. Garg, “Early detection of pediatric seizures in the high gamma band,” IEEE Access, vol. 9, pp. 85209–85216, 2021.
Anibal Romney And V. Manian “Optimizing Seizure Prediction From Reduced Scalp EEG Channels Based on Spectral Features and MAML”, VOLUME 9, 2021 Digital Object Identifier 10.1109/ACCESS.2021.3134166
Ahmed Abdelhameed, Magdy Bayoumi; “A Deep Learning Approach for Automatic Seizure Detection in Children with Epilepsy”; Frontiers, Computational Neuroscience, 8th April 2021
Khansa Rasheed , Junaid Qadir , Senior Member, IEEE, Terence J. O’Brien, Levin Kuhlmann , And Adeel Razi , Member, IEEE “A Generative Model To Synthesize EEG Data For Epileptic Seizure Prediction” IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, VOL. 29, 2021
Xinwu Yang , Jiaqi Zhao , Qi Sun, Jianbo Lu, and Xu Ma “An Effective Dual Self-Attention Residual Network for Seizure Prediction” IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, VOL. 29, 2021
Ines Assalia , Ines Jlassia , Mouna Aissib , Ahmed Ghazi Blaiechc , Marcel Carrèred , Mohamed Hedi Bedouia “Comparison by multivariate auto-regressive method of seizure prediction for real patients and virtual patients”, HAL Id: hal-03553050 https://hal.archives-ouvertes.fr/hal-03553050 Submitted on 9 Mar 2022
Dorsa EP Moghaddam, Sameer A Sheth, Zulfi Haneef , Jay Gavvala, Behnaam Aazhang; “Epileptic seizure prediction using spectral width of the covariance matrix”; Journal of Neural Engineering, IOP , 5th April 2022
Md Abu Sayeed, Saraju P. Mohanty, Elias Kougianos, Hitten P. Zaveri; “Neuro-Detect: A Machine Learning Based Fast and Accurate Seizure Detection System in the IoMT”; IEEE, 2019
Yannick Roy, Hubert Banville, Isabela Albuquerque, Alexandre Gramfort, Tiago H Falk, Jocelyn Faubert; “Deep learning-based electroencephalography analysis: a systematic review”; IOP Publishing, Journal of Neural Engineering, 2019
Mohammad Khubeb Siddiqui, Ruben Morales‑Menendez, Xiaodi Huang, Nasir Hussain, “A review of epileptic seizure detection using machine learning classifiers”; Springer, Brain Informatics, 2020
Kemal Akyol, Ümit Atila, “Comparing The Effect of Under-Sampling and Over-Sampling on Traditional Machine Learning Algorithms for Epileptic Seizure Detection”, Academic Platform Journal of Engineering and Science 8-2, 279-285, 2020.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
