A Modified Gated Recurrent Unit Approach for Epileptic Electroencephalography Classification

Epilepsy is one of the most severe non-communicable brain disorders associated with sudden attacks. Electroencephalography (EEG), a non-invasive technique, records brain activities, and these recordings are routinely used for the clinical evaluation of epilepsy. EEG signal analysis for seizure ident...

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Main Authors: Prakash, Vinod, Kumar, Dharmender
Format: Article
Language:English
Published: Universiti Utara Malaysia Press 2023
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Online Access:https://repo.uum.edu.my/id/eprint/29906/1/JICT%2022%2004%202023%20587-617.pdf
https://doi.org/10.32890/jict2023.22.4.3
https://repo.uum.edu.my/id/eprint/29906/
https://e-journal.uum.edu.my/index.php/jict/article/view/15364
https://doi.org/10.32890/jict2023.22.4.3
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Institution: Universiti Utara Malaysia
Language: English
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spelling my.uum.repo.299062023-11-05T10:09:39Z https://repo.uum.edu.my/id/eprint/29906/ A Modified Gated Recurrent Unit Approach for Epileptic Electroencephalography Classification Prakash, Vinod Kumar, Dharmender T Technology (General) Epilepsy is one of the most severe non-communicable brain disorders associated with sudden attacks. Electroencephalography (EEG), a non-invasive technique, records brain activities, and these recordings are routinely used for the clinical evaluation of epilepsy. EEG signal analysis for seizure identification relies on expert manual examination, which is labour-intensive, time-consuming, and prone to human error. To overcome these limitations, researchers have proposed machine learning and deep learning approaches. Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) have shown significant results in automating seizure prediction, but due to complex gated mechanisms and the storage of excessive redundant information, these approaches face slow convergence and a low learning rate. The proposed modified GRU approach includes an improved update gate unit that adjusts the update gate based on the output of the reset gate. By decreasing the amount of superfluous data in the reset gate, convergence is speeded, which improves both learning efficiency and the accuracy of epilepsy seizure prediction. The performance of the proposed approach is verified on a publicly available epileptic EEG dataset collected from the University of California, Irvine machine learning repository (UCI) in terms of performance metrics such as accuracy, precision, recall, and F1 score when it comes to diagnosing epileptic seizures. The proposed modified GRU has obtained 98.84% accuracy, 96.9% precision, 97.1 recall, and 97% F1 score. The performance results are significant because they could enhance the diagnosis and treatment of neurological disorders, leading to better patient outcomes. Universiti Utara Malaysia Press 2023 Article PeerReviewed application/pdf en cc4_by https://repo.uum.edu.my/id/eprint/29906/1/JICT%2022%2004%202023%20587-617.pdf Prakash, Vinod and Kumar, Dharmender (2023) A Modified Gated Recurrent Unit Approach for Epileptic Electroencephalography Classification. Journal of Information and Communication Technology, 22 (4). pp. 587-617. ISSN 2180-3862 https://e-journal.uum.edu.my/index.php/jict/article/view/15364 https://doi.org/10.32890/jict2023.22.4.3 https://doi.org/10.32890/jict2023.22.4.3
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutional Repository
url_provider http://repo.uum.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Prakash, Vinod
Kumar, Dharmender
A Modified Gated Recurrent Unit Approach for Epileptic Electroencephalography Classification
description Epilepsy is one of the most severe non-communicable brain disorders associated with sudden attacks. Electroencephalography (EEG), a non-invasive technique, records brain activities, and these recordings are routinely used for the clinical evaluation of epilepsy. EEG signal analysis for seizure identification relies on expert manual examination, which is labour-intensive, time-consuming, and prone to human error. To overcome these limitations, researchers have proposed machine learning and deep learning approaches. Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) have shown significant results in automating seizure prediction, but due to complex gated mechanisms and the storage of excessive redundant information, these approaches face slow convergence and a low learning rate. The proposed modified GRU approach includes an improved update gate unit that adjusts the update gate based on the output of the reset gate. By decreasing the amount of superfluous data in the reset gate, convergence is speeded, which improves both learning efficiency and the accuracy of epilepsy seizure prediction. The performance of the proposed approach is verified on a publicly available epileptic EEG dataset collected from the University of California, Irvine machine learning repository (UCI) in terms of performance metrics such as accuracy, precision, recall, and F1 score when it comes to diagnosing epileptic seizures. The proposed modified GRU has obtained 98.84% accuracy, 96.9% precision, 97.1 recall, and 97% F1 score. The performance results are significant because they could enhance the diagnosis and treatment of neurological disorders, leading to better patient outcomes.
format Article
author Prakash, Vinod
Kumar, Dharmender
author_facet Prakash, Vinod
Kumar, Dharmender
author_sort Prakash, Vinod
title A Modified Gated Recurrent Unit Approach for Epileptic Electroencephalography Classification
title_short A Modified Gated Recurrent Unit Approach for Epileptic Electroencephalography Classification
title_full A Modified Gated Recurrent Unit Approach for Epileptic Electroencephalography Classification
title_fullStr A Modified Gated Recurrent Unit Approach for Epileptic Electroencephalography Classification
title_full_unstemmed A Modified Gated Recurrent Unit Approach for Epileptic Electroencephalography Classification
title_sort modified gated recurrent unit approach for epileptic electroencephalography classification
publisher Universiti Utara Malaysia Press
publishDate 2023
url https://repo.uum.edu.my/id/eprint/29906/1/JICT%2022%2004%202023%20587-617.pdf
https://doi.org/10.32890/jict2023.22.4.3
https://repo.uum.edu.my/id/eprint/29906/
https://e-journal.uum.edu.my/index.php/jict/article/view/15364
https://doi.org/10.32890/jict2023.22.4.3
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