Graphical Insight: Revolutionizing Seizure Detection with EEG Representation
<p dir="ltr">Epilepsy is characterized by recurring seizures that result from abnormal electrical activity in the brain. These seizures manifest as various symptoms including muscle contractions and loss of consciousness. The challenging task of detecting epileptic seizures involves...
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| مؤلفون آخرون: | , |
| منشور في: |
2024
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| _version_ | 1864513509840125952 |
|---|---|
| author | Muhammad Awais (263096) |
| author2 | Samir Brahim Belhaouari (9427347) Khelil Kassoul (18441114) |
| author2_role | author author |
| author_facet | Muhammad Awais (263096) Samir Brahim Belhaouari (9427347) Khelil Kassoul (18441114) |
| author_role | author |
| dc.creator.none.fl_str_mv | Muhammad Awais (263096) Samir Brahim Belhaouari (9427347) Khelil Kassoul (18441114) |
| dc.date.none.fl_str_mv | 2024-06-10T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/biomedicines12061283 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Graphical_Insight_Revolutionizing_Seizure_Detection_with_EEG_Representation/26403760 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Neurosciences Engineering Biomedical engineering EEG signal GNN seizure detection epilepsy graph convolutional network |
| dc.title.none.fl_str_mv | Graphical Insight: Revolutionizing Seizure Detection with EEG Representation |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Epilepsy is characterized by recurring seizures that result from abnormal electrical activity in the brain. These seizures manifest as various symptoms including muscle contractions and loss of consciousness. The challenging task of detecting epileptic seizures involves classifying electroencephalography (EEG) signals into ictal (seizure) and interictal (non-seizure) classes. This classification is crucial because it distinguishes between the states of seizure and seizure-free periods in patients with epilepsy. Our study presents an innovative approach for detecting seizures and neurological diseases using EEG signals by leveraging graph neural networks. This method effectively addresses EEG data processing challenges. We construct a graph representation of EEG signals by extracting features such as frequency-based, statistical-based, and Daubechies wavelet transform features. This graph representation allows for potential differentiation between seizure and non-seizure signals through visual inspection of the extracted features. To enhance seizure detection accuracy, we employ two models: one combining a graph convolutional network (GCN) with long short-term memory (LSTM) and the other combining a GCN with balanced random forest (BRF). Our experimental results reveal that both models significantly improve seizure detection accuracy, surpassing previous methods. Despite simplifying our approach by reducing channels, our research reveals a consistent performance, showing a significant advancement in neurodegenerative disease detection. Our models accurately identify seizures in EEG signals, underscoring the potential of graph neural networks. The streamlined method not only maintains effectiveness with fewer channels but also offers a visually distinguishable approach for discerning seizure classes. This research opens avenues for EEG analysis, emphasizing the impact of graph representations in advancing our understanding of neurodegenerative diseases.</p><h2>Other Information</h2><p dir="ltr">Published in: Biomedicines<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/biomedicines12061283" target="_blank">https://dx.doi.org/10.3390/biomedicines12061283</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_695efac7345d7509d5caaba4ec5b66ef |
| identifier_str_mv | 10.3390/biomedicines12061283 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26403760 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Graphical Insight: Revolutionizing Seizure Detection with EEG RepresentationMuhammad Awais (263096)Samir Brahim Belhaouari (9427347)Khelil Kassoul (18441114)Biomedical and clinical sciencesNeurosciencesEngineeringBiomedical engineeringEEG signalGNNseizure detectionepilepsygraph convolutional network<p dir="ltr">Epilepsy is characterized by recurring seizures that result from abnormal electrical activity in the brain. These seizures manifest as various symptoms including muscle contractions and loss of consciousness. The challenging task of detecting epileptic seizures involves classifying electroencephalography (EEG) signals into ictal (seizure) and interictal (non-seizure) classes. This classification is crucial because it distinguishes between the states of seizure and seizure-free periods in patients with epilepsy. Our study presents an innovative approach for detecting seizures and neurological diseases using EEG signals by leveraging graph neural networks. This method effectively addresses EEG data processing challenges. We construct a graph representation of EEG signals by extracting features such as frequency-based, statistical-based, and Daubechies wavelet transform features. This graph representation allows for potential differentiation between seizure and non-seizure signals through visual inspection of the extracted features. To enhance seizure detection accuracy, we employ two models: one combining a graph convolutional network (GCN) with long short-term memory (LSTM) and the other combining a GCN with balanced random forest (BRF). Our experimental results reveal that both models significantly improve seizure detection accuracy, surpassing previous methods. Despite simplifying our approach by reducing channels, our research reveals a consistent performance, showing a significant advancement in neurodegenerative disease detection. Our models accurately identify seizures in EEG signals, underscoring the potential of graph neural networks. The streamlined method not only maintains effectiveness with fewer channels but also offers a visually distinguishable approach for discerning seizure classes. This research opens avenues for EEG analysis, emphasizing the impact of graph representations in advancing our understanding of neurodegenerative diseases.</p><h2>Other Information</h2><p dir="ltr">Published in: Biomedicines<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/biomedicines12061283" target="_blank">https://dx.doi.org/10.3390/biomedicines12061283</a></p>2024-06-10T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/biomedicines12061283https://figshare.com/articles/journal_contribution/Graphical_Insight_Revolutionizing_Seizure_Detection_with_EEG_Representation/26403760CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/264037602024-06-10T09:00:00Z |
| spellingShingle | Graphical Insight: Revolutionizing Seizure Detection with EEG Representation Muhammad Awais (263096) Biomedical and clinical sciences Neurosciences Engineering Biomedical engineering EEG signal GNN seizure detection epilepsy graph convolutional network |
| status_str | publishedVersion |
| title | Graphical Insight: Revolutionizing Seizure Detection with EEG Representation |
| title_full | Graphical Insight: Revolutionizing Seizure Detection with EEG Representation |
| title_fullStr | Graphical Insight: Revolutionizing Seizure Detection with EEG Representation |
| title_full_unstemmed | Graphical Insight: Revolutionizing Seizure Detection with EEG Representation |
| title_short | Graphical Insight: Revolutionizing Seizure Detection with EEG Representation |
| title_sort | Graphical Insight: Revolutionizing Seizure Detection with EEG Representation |
| topic | Biomedical and clinical sciences Neurosciences Engineering Biomedical engineering EEG signal GNN seizure detection epilepsy graph convolutional network |