Advanced Machine Learning and Deep Learning Approaches for Accurate Epileptic Seizure Detection Using EEG Signals.
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| Title: | Advanced Machine Learning and Deep Learning Approaches for Accurate Epileptic Seizure Detection Using EEG Signals. |
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| Authors: | Garg, Malika1 (AUTHOR) malikagarg.phd19ece@pec.edu.in, Kaur, Jasbir1 (AUTHOR), Prakash, Neelam Rup1 (AUTHOR) |
| Source: | International Journal of Imaging Systems & Technology. May2026, Vol. 36 Issue 3, p1-19. 19p. |
| Subjects: | Epilepsy, Biomedical signal processing, Classification algorithms, Machine learning, Artificial neural networks, Deep learning, Electrophysiology |
| Abstract: | Epilepsy, a neurological disorder that is caused by improper brain activity that results in seizures, is prevalent in millions of persons across the world and for which diagnosis is extremely crucial for treatment. Early detection of epileptic seizures is paramount as it enables timely intervention, improves quality of life, and prevents potential risks during seizures. Machine learning (ML) and deep learning (DL) algorithms have emerged as powerful tools in revolutionizing medical field, particularly in domains involving images, signals, and other types of visual representations. In our study, we have utilized the capability of these algorithms in exploring their impact on the epileptic seizure detection with the help of EEG Signals. Nine DL models, namely LSTM, GRU, Bi‐LSTM, CNN, FCNN, Hybrid CNN‐LSTM, EEGNet, Shallow ConvNet, and Hybrid CNN‐GRU and seven machine learning models that is, Random Forest, XGBoost, KNN, Logistic Regression, Naïve Bayes, Decision Tree, and Stacking Ensemble are employed for classification of epileptic seizures on EEG signal dataset. All the models have been evaluated using the standard performance metrics for determining the effectiveness of these models on the epileptic seizure classification as epileptic and nonepileptic. Visual representations like accuracy‐loss graphs and confusion matrix were also generated for better visual understanding of the performance of the model. Among all DL Models, CNN emerged as the best performing model with 85% accuracy, whereas, among ML models, XGBoost performs best with accuracy of 88%. The study underscores the potential and effectiveness of ML and DL models in detecting complex patterns and generating predictive insights for classification and detection of epileptic seizure from EEG signals. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Imaging Systems & Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194052859 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Advanced Machine Learning and Deep Learning Approaches for Accurate Epileptic Seizure Detection Using EEG Signals. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Garg%2C+Malika%22">Garg, Malika</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> malikagarg.phd19ece@pec.edu.in</i><br /><searchLink fieldCode="AR" term="%22Kaur%2C+Jasbir%22">Kaur, Jasbir</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Prakash%2C+Neelam+Rup%22">Prakash, Neelam Rup</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Imaging+Systems+%26+Technology%22">International Journal of Imaging Systems & Technology</searchLink>. May2026, Vol. 36 Issue 3, p1-19. 19p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Epilepsy%22">Epilepsy</searchLink><br /><searchLink fieldCode="DE" term="%22Biomedical+signal+processing%22">Biomedical signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Classification+algorithms%22">Classification algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Electrophysiology%22">Electrophysiology</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Epilepsy, a neurological disorder that is caused by improper brain activity that results in seizures, is prevalent in millions of persons across the world and for which diagnosis is extremely crucial for treatment. Early detection of epileptic seizures is paramount as it enables timely intervention, improves quality of life, and prevents potential risks during seizures. Machine learning (ML) and deep learning (DL) algorithms have emerged as powerful tools in revolutionizing medical field, particularly in domains involving images, signals, and other types of visual representations. In our study, we have utilized the capability of these algorithms in exploring their impact on the epileptic seizure detection with the help of EEG Signals. Nine DL models, namely LSTM, GRU, Bi‐LSTM, CNN, FCNN, Hybrid CNN‐LSTM, EEGNet, Shallow ConvNet, and Hybrid CNN‐GRU and seven machine learning models that is, Random Forest, XGBoost, KNN, Logistic Regression, Naïve Bayes, Decision Tree, and Stacking Ensemble are employed for classification of epileptic seizures on EEG signal dataset. All the models have been evaluated using the standard performance metrics for determining the effectiveness of these models on the epileptic seizure classification as epileptic and nonepileptic. Visual representations like accuracy‐loss graphs and confusion matrix were also generated for better visual understanding of the performance of the model. Among all DL Models, CNN emerged as the best performing model with 85% accuracy, whereas, among ML models, XGBoost performs best with accuracy of 88%. The study underscores the potential and effectiveness of ML and DL models in detecting complex patterns and generating predictive insights for classification and detection of epileptic seizure from EEG signals. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Imaging Systems & Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/ima.70342 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 1 Subjects: – SubjectFull: Epilepsy Type: general – SubjectFull: Biomedical signal processing Type: general – SubjectFull: Classification algorithms Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Electrophysiology Type: general Titles: – TitleFull: Advanced Machine Learning and Deep Learning Approaches for Accurate Epileptic Seizure Detection Using EEG Signals. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Garg, Malika – PersonEntity: Name: NameFull: Kaur, Jasbir – PersonEntity: Name: NameFull: Prakash, Neelam Rup IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 08999457 Numbering: – Type: volume Value: 36 – Type: issue Value: 3 Titles: – TitleFull: International Journal of Imaging Systems & Technology Type: main |
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