Diagnosis of epileptic seizure neurological condition using EEG signal: a multi-model algorithm
IntroductionAffecting millions of individuals worldwide, epilepsy is a neurological condition marked by repeated convulsions. Monitoring brain activity and identifying seizures depends much on electroencephalography (EEG). An essential step that may help clinicians identify and treat epileptic seizu...
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Frontiers Media S.A.
2025-05-01
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1577474/full |
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| author | Mosleh Hmoud Al-Adhaileh Mosleh Hmoud Al-Adhaileh Sultan Ahmad Alhasan A. Alharbi Mohammed Alarfaj Mohammed Alarfaj Mukta Dhopeshwarkar Theyazn H. H. Aldhyani |
| author_facet | Mosleh Hmoud Al-Adhaileh Mosleh Hmoud Al-Adhaileh Sultan Ahmad Alhasan A. Alharbi Mohammed Alarfaj Mohammed Alarfaj Mukta Dhopeshwarkar Theyazn H. H. Aldhyani |
| author_sort | Mosleh Hmoud Al-Adhaileh |
| collection | DOAJ |
| description | IntroductionAffecting millions of individuals worldwide, epilepsy is a neurological condition marked by repeated convulsions. Monitoring brain activity and identifying seizures depends much on electroencephalography (EEG). An essential step that may help clinicians identify and treat epileptic seizures is the differentiation between epileptic and non-epileptic signals by use of epileptic seizure detection categorization.MethodsIn this work, we investigated Machine learning algorithms including Random Forest, Gradient Boosting, and K-Nearest Neighbors, alongside advanced DL architectures such as Long Short-Term Memory networks and Long-term Recurrent Convolutional Networks for detecting epileptic seizures in terms of difficulties and procedures evolved depending on EEG data. The EEG data classification by applying ML and DL framework to improve the accuracy of seizure detection. The EEG dataset consisted of 102 patients (55 seizure and 47 non-seizure cases), and the data underwent comprehensive preprocessing, including noise removal, frequency band extraction, and data balancing using SMOTE to address class imbalance. Key features, including delta, theta, alpha, beta, and gamma bands, as well as spectral entropy, were extracted to aid in the classification process.ResultsA comparative analysis was conducted, resulting in high classification accuracy, with the Random Forest model achieving the best results at 99.9% accuracy.DiscussionThe study demonstrates the potential of EEG data for reliable seizure detection while emphasizing the need for further development of more practical and non-invasive monitoring systems for real-world applications. |
| format | Article |
| id | doaj-art-1b02cd6553be4925a92b9ed1fda2b423 |
| institution | OA Journals |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-1b02cd6553be4925a92b9ed1fda2b4232025-08-20T02:32:19ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-05-011210.3389/fmed.2025.15774741577474Diagnosis of epileptic seizure neurological condition using EEG signal: a multi-model algorithmMosleh Hmoud Al-Adhaileh0Mosleh Hmoud Al-Adhaileh1Sultan Ahmad2Alhasan A. Alharbi3Mohammed Alarfaj4Mohammed Alarfaj5Mukta Dhopeshwarkar6Theyazn H. H. Aldhyani7King Salman Center for Disability Research, Riyadh, Saudi ArabiaDeanship of E-Learning and Information Technology, King Faisal University, Al-Ahsa, Saudi ArabiaDepartment of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi ArabiaDepartment of Computer Science, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, IndiaKing Salman Center for Disability Research, Riyadh, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering, King Faisal University, Al-Ahsa, Saudi ArabiaDepartment of Computer Science, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, IndiaApplied College in Abqaiq, King Faisal University, Al-Ahsa, Saudi ArabiaIntroductionAffecting millions of individuals worldwide, epilepsy is a neurological condition marked by repeated convulsions. Monitoring brain activity and identifying seizures depends much on electroencephalography (EEG). An essential step that may help clinicians identify and treat epileptic seizures is the differentiation between epileptic and non-epileptic signals by use of epileptic seizure detection categorization.MethodsIn this work, we investigated Machine learning algorithms including Random Forest, Gradient Boosting, and K-Nearest Neighbors, alongside advanced DL architectures such as Long Short-Term Memory networks and Long-term Recurrent Convolutional Networks for detecting epileptic seizures in terms of difficulties and procedures evolved depending on EEG data. The EEG data classification by applying ML and DL framework to improve the accuracy of seizure detection. The EEG dataset consisted of 102 patients (55 seizure and 47 non-seizure cases), and the data underwent comprehensive preprocessing, including noise removal, frequency band extraction, and data balancing using SMOTE to address class imbalance. Key features, including delta, theta, alpha, beta, and gamma bands, as well as spectral entropy, were extracted to aid in the classification process.ResultsA comparative analysis was conducted, resulting in high classification accuracy, with the Random Forest model achieving the best results at 99.9% accuracy.DiscussionThe study demonstrates the potential of EEG data for reliable seizure detection while emphasizing the need for further development of more practical and non-invasive monitoring systems for real-world applications.https://www.frontiersin.org/articles/10.3389/fmed.2025.1577474/fullelectroencephalographyEEG data classificationseizure detectionepilepsySMOTE |
| spellingShingle | Mosleh Hmoud Al-Adhaileh Mosleh Hmoud Al-Adhaileh Sultan Ahmad Alhasan A. Alharbi Mohammed Alarfaj Mohammed Alarfaj Mukta Dhopeshwarkar Theyazn H. H. Aldhyani Diagnosis of epileptic seizure neurological condition using EEG signal: a multi-model algorithm Frontiers in Medicine electroencephalography EEG data classification seizure detection epilepsy SMOTE |
| title | Diagnosis of epileptic seizure neurological condition using EEG signal: a multi-model algorithm |
| title_full | Diagnosis of epileptic seizure neurological condition using EEG signal: a multi-model algorithm |
| title_fullStr | Diagnosis of epileptic seizure neurological condition using EEG signal: a multi-model algorithm |
| title_full_unstemmed | Diagnosis of epileptic seizure neurological condition using EEG signal: a multi-model algorithm |
| title_short | Diagnosis of epileptic seizure neurological condition using EEG signal: a multi-model algorithm |
| title_sort | diagnosis of epileptic seizure neurological condition using eeg signal a multi model algorithm |
| topic | electroencephalography EEG data classification seizure detection epilepsy SMOTE |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1577474/full |
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