Enhancing seizure detection with hybrid XGBoost and recurrent neural networks
Epileptic seizures are sudden and unpredictable, posing serious health risks and significantly affecting the quality of life of patients. An accurate and timely prediction system can help mitigate these risks by enabling preventive measures and improving patient safety. This study investigates machi...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Neuroscience Informatics |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772528625000214 |
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| author | Santushti Santosh Betgeri Madhu Shukla Dinesh Kumar Surbhi B. Khan Muhammad Attique Khan Nora A. Alkhaldi |
| author_facet | Santushti Santosh Betgeri Madhu Shukla Dinesh Kumar Surbhi B. Khan Muhammad Attique Khan Nora A. Alkhaldi |
| author_sort | Santushti Santosh Betgeri |
| collection | DOAJ |
| description | Epileptic seizures are sudden and unpredictable, posing serious health risks and significantly affecting the quality of life of patients. An accurate and timely prediction system can help mitigate these risks by enabling preventive measures and improving patient safety. This study investigates machine learning and deep learning algorithms for seizure prediction, comparing their effectiveness on a large EEG dataset of epileptic patients. Signal processing techniques were applied to enhance data quality, and all models were trained on the same dataset for binary classification. Sixteen models were evaluated, including traditional classifiers such as Logistic Regression, K-Nearest Neighbors, Decision Trees, ensemble methods that include Random Forest, Gradient Boosting, and advanced techniques such as Extreme Gradient Boosting, Support Vector Machines, Gated Recurrent Units, and Long Short-Term Memory networks. Performance was assessed using multiple evaluation metrics on both training and validation datasets. While simpler models showed varied accuracy, ensemble and deep learning models performed significantly better, with hybrid approaches demonstrating strong generalization. Results show that whereas ensemble and deep learning models far exceeded simpler models, their accuracy varied. AUC of 0.995 and accuracy of 98.2% on validation data and 0.994 AUC with 96.8% accuracy on test data were obtained by the proposed hybrid Model integrating XGBoost with RNN-based architectures (LSTM and GRU). High recall (96.2%) shown by the Model guarantees minimal false negatives and is important for clinical uses. Furthermore, EEG signal preprocessing methods improved data quality, raising classification accuracy. This Model can be implemented for real-time monitoring using wearable devices, enabling continuous patient observation and remote healthcare applications. |
| format | Article |
| id | doaj-art-da4d1c81c0cf470b9ef9e8d3c4e414f9 |
| institution | DOAJ |
| issn | 2772-5286 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Neuroscience Informatics |
| spelling | doaj-art-da4d1c81c0cf470b9ef9e8d3c4e414f92025-08-20T03:13:30ZengElsevierNeuroscience Informatics2772-52862025-06-015210020610.1016/j.neuri.2025.100206Enhancing seizure detection with hybrid XGBoost and recurrent neural networksSantushti Santosh Betgeri0Madhu Shukla1Dinesh Kumar2Surbhi B. Khan3Muhammad Attique Khan4Nora A. Alkhaldi5Department of Computer Engineering, Marwadi University, Rajkot, Gujarat, 360003, IndiaDepartment of Computer Engineering, Marwadi University, Rajkot, Gujarat, 360003, India; Corresponding authors.School of Artificial Intelligence, Bennett University (The Times Group), Greater Noida, UP, 201310, IndiaSchool of Science, Engineering and Environment, University of Salford, United Kingdom; Corresponding authors.Center of AI, Prince Mohammad Bin Fahd University, Kingdom of Saudi ArabiaDepartment of Computer Science, College of Computer Science and Information Technology, King Faisal University, Al Ahsa 36291, Kingdom of Saudi ArabiaEpileptic seizures are sudden and unpredictable, posing serious health risks and significantly affecting the quality of life of patients. An accurate and timely prediction system can help mitigate these risks by enabling preventive measures and improving patient safety. This study investigates machine learning and deep learning algorithms for seizure prediction, comparing their effectiveness on a large EEG dataset of epileptic patients. Signal processing techniques were applied to enhance data quality, and all models were trained on the same dataset for binary classification. Sixteen models were evaluated, including traditional classifiers such as Logistic Regression, K-Nearest Neighbors, Decision Trees, ensemble methods that include Random Forest, Gradient Boosting, and advanced techniques such as Extreme Gradient Boosting, Support Vector Machines, Gated Recurrent Units, and Long Short-Term Memory networks. Performance was assessed using multiple evaluation metrics on both training and validation datasets. While simpler models showed varied accuracy, ensemble and deep learning models performed significantly better, with hybrid approaches demonstrating strong generalization. Results show that whereas ensemble and deep learning models far exceeded simpler models, their accuracy varied. AUC of 0.995 and accuracy of 98.2% on validation data and 0.994 AUC with 96.8% accuracy on test data were obtained by the proposed hybrid Model integrating XGBoost with RNN-based architectures (LSTM and GRU). High recall (96.2%) shown by the Model guarantees minimal false negatives and is important for clinical uses. Furthermore, EEG signal preprocessing methods improved data quality, raising classification accuracy. This Model can be implemented for real-time monitoring using wearable devices, enabling continuous patient observation and remote healthcare applications.http://www.sciencedirect.com/science/article/pii/S2772528625000214 |
| spellingShingle | Santushti Santosh Betgeri Madhu Shukla Dinesh Kumar Surbhi B. Khan Muhammad Attique Khan Nora A. Alkhaldi Enhancing seizure detection with hybrid XGBoost and recurrent neural networks Neuroscience Informatics |
| title | Enhancing seizure detection with hybrid XGBoost and recurrent neural networks |
| title_full | Enhancing seizure detection with hybrid XGBoost and recurrent neural networks |
| title_fullStr | Enhancing seizure detection with hybrid XGBoost and recurrent neural networks |
| title_full_unstemmed | Enhancing seizure detection with hybrid XGBoost and recurrent neural networks |
| title_short | Enhancing seizure detection with hybrid XGBoost and recurrent neural networks |
| title_sort | enhancing seizure detection with hybrid xgboost and recurrent neural networks |
| url | http://www.sciencedirect.com/science/article/pii/S2772528625000214 |
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