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Automatic diagnosis of epileptic seizures using entropy-based features and multimodel deep learning approaches

Epilepsy is one of the most common brain diseases, characterised by repeated seizures that occur on a regular basis. During a seizure, a patient's muscles flex uncontrollably, causing a loss of mobility and balance, which can be harmful or even fatal. Developing an automatic approach for warnin...

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Main Authors: Al-Qazzaz, Noor Kamal, Alrahhal, Maher, Jaafer, Sumai Hamad, Mohd Ali, Sawal Hamid, Ahmad, Siti Anom
Format: Article
Language:English
Published: Elsevier 2024
Online Access:http://psasir.upm.edu.my/id/eprint/113638/1/113638.pdf
http://psasir.upm.edu.my/id/eprint/113638/
https://www.sciencedirect.com/science/article/pii/S1350453324001073?via%3Dihub
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spelling my.upm.eprints.1136382024-11-14T02:39:01Z http://psasir.upm.edu.my/id/eprint/113638/ Automatic diagnosis of epileptic seizures using entropy-based features and multimodel deep learning approaches Al-Qazzaz, Noor Kamal Alrahhal, Maher Jaafer, Sumai Hamad Mohd Ali, Sawal Hamid Ahmad, Siti Anom Epilepsy is one of the most common brain diseases, characterised by repeated seizures that occur on a regular basis. During a seizure, a patient's muscles flex uncontrollably, causing a loss of mobility and balance, which can be harmful or even fatal. Developing an automatic approach for warning patients of oncoming seizures necessitates substantial research. Analyzing the electroencephalogram (EEG) output from the human brain's scalp region can help predict seizures. EEG data were analyzed to extract time domain features such as Hurst exponent (Hur), Tsallis entropy (TsEn), enhanced permutation entropy (impe), and amplitude-aware permutation entropy (AAPE). In order to automatically diagnose epileptic seizure in children from normal children, this study conducted two sessions. In the first session, the extracted features from the EEG dataset were classified using three machine learning (ML)-based models, including support vector machine (SVM), K nearest neighbor (KNN), or decision tree (DT), and in the second session, the dataset was classified using three deep learning (DL)-based recurrent neural network (RNN) classifiers in The EEG dataset was obtained from the Neurology Clinic of the Ibn Rushd Training Hospital. In this regard, extensive explanations and research from the time domain and entropy characteristics demonstrate that employing GRU, LSTM, and BiLSTM RNN deep learning classifiers on the All−time−entropy fusion feature improves the final classification results. Elsevier 2024 Article PeerReviewed text en cc_by_nc_4 http://psasir.upm.edu.my/id/eprint/113638/1/113638.pdf Al-Qazzaz, Noor Kamal and Alrahhal, Maher and Jaafer, Sumai Hamad and Mohd Ali, Sawal Hamid and Ahmad, Siti Anom (2024) Automatic diagnosis of epileptic seizures using entropy-based features and multimodel deep learning approaches. Medical Engineering & Physics, 130. art. no. 104206. pp. 1-10. ISSN 1350-4533; eISSN: 1873-4030 https://www.sciencedirect.com/science/article/pii/S1350453324001073?via%3Dihub 10.1016/j.medengphy.2024.104206
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Epilepsy is one of the most common brain diseases, characterised by repeated seizures that occur on a regular basis. During a seizure, a patient's muscles flex uncontrollably, causing a loss of mobility and balance, which can be harmful or even fatal. Developing an automatic approach for warning patients of oncoming seizures necessitates substantial research. Analyzing the electroencephalogram (EEG) output from the human brain's scalp region can help predict seizures. EEG data were analyzed to extract time domain features such as Hurst exponent (Hur), Tsallis entropy (TsEn), enhanced permutation entropy (impe), and amplitude-aware permutation entropy (AAPE). In order to automatically diagnose epileptic seizure in children from normal children, this study conducted two sessions. In the first session, the extracted features from the EEG dataset were classified using three machine learning (ML)-based models, including support vector machine (SVM), K nearest neighbor (KNN), or decision tree (DT), and in the second session, the dataset was classified using three deep learning (DL)-based recurrent neural network (RNN) classifiers in The EEG dataset was obtained from the Neurology Clinic of the Ibn Rushd Training Hospital. In this regard, extensive explanations and research from the time domain and entropy characteristics demonstrate that employing GRU, LSTM, and BiLSTM RNN deep learning classifiers on the All−time−entropy fusion feature improves the final classification results.
format Article
author Al-Qazzaz, Noor Kamal
Alrahhal, Maher
Jaafer, Sumai Hamad
Mohd Ali, Sawal Hamid
Ahmad, Siti Anom
spellingShingle Al-Qazzaz, Noor Kamal
Alrahhal, Maher
Jaafer, Sumai Hamad
Mohd Ali, Sawal Hamid
Ahmad, Siti Anom
Automatic diagnosis of epileptic seizures using entropy-based features and multimodel deep learning approaches
author_facet Al-Qazzaz, Noor Kamal
Alrahhal, Maher
Jaafer, Sumai Hamad
Mohd Ali, Sawal Hamid
Ahmad, Siti Anom
author_sort Al-Qazzaz, Noor Kamal
title Automatic diagnosis of epileptic seizures using entropy-based features and multimodel deep learning approaches
title_short Automatic diagnosis of epileptic seizures using entropy-based features and multimodel deep learning approaches
title_full Automatic diagnosis of epileptic seizures using entropy-based features and multimodel deep learning approaches
title_fullStr Automatic diagnosis of epileptic seizures using entropy-based features and multimodel deep learning approaches
title_full_unstemmed Automatic diagnosis of epileptic seizures using entropy-based features and multimodel deep learning approaches
title_sort automatic diagnosis of epileptic seizures using entropy-based features and multimodel deep learning approaches
publisher Elsevier
publishDate 2024
url http://psasir.upm.edu.my/id/eprint/113638/1/113638.pdf
http://psasir.upm.edu.my/id/eprint/113638/
https://www.sciencedirect.com/science/article/pii/S1350453324001073?via%3Dihub
_version_ 1816132755318112256
score 13.223943

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