Feature fusion ensemble classification approach for epileptic seizure prediction using electroencephalographic bio-signals
IntroductionEpilepsy is a neurological disorder in which patients experience recurrent seizures, with the frequency of occurrence more than twice a day, which highly affects a patient's life. In recent years, multiple researchers have proposed multiple machine learning and deep learning-based m...
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Frontiers Media S.A.
2025-08-01
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1566870/full |
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| author | Yazeed Alkhrijah Yazeed Alkhrijah Shehzad Khalid Shehzad Khalid Syed Muhammad Usman Amina Jameel Muhammad Zubair Haya Aldossary Aamir Anwar Saad Arif |
| author_facet | Yazeed Alkhrijah Yazeed Alkhrijah Shehzad Khalid Shehzad Khalid Syed Muhammad Usman Amina Jameel Muhammad Zubair Haya Aldossary Aamir Anwar Saad Arif |
| author_sort | Yazeed Alkhrijah |
| collection | DOAJ |
| description | IntroductionEpilepsy is a neurological disorder in which patients experience recurrent seizures, with the frequency of occurrence more than twice a day, which highly affects a patient's life. In recent years, multiple researchers have proposed multiple machine learning and deep learning-based methods to predict the onset of seizures using electroencephalogram (EEG) signals before they occur; however, robust preprocessing to mitigate the effect of noise, channel selection to reduce dimensionality, and feature extraction remain challenges in accurate prediction.MethodsThis study proposes a novel method for accurately predicting epileptic seizures. In the first step, a Butterworth filter is applied, followed by a wavelet and a Fourier transform for the denoising of EEG signals. A non-overlapping window of 15 s is selected to segment the EEG signals, and an optimal spatial filter is applied to reduce the dimensionality. Handcrafted features, including both time and frequency domains, have been extracted and concatenated with the customized one-dimensional convolutional neural network-based features to form a comprehensive feature vector. It is then fed into three classifiers, including support vector machines, random forest, and long short-term memory (LSTM) units. The output of these classifiers is then fed into the model-agnostic meta learner ensemble classifier with LSTM as the base classifier for the final prediction of interictal and preictal states.ResultsThe proposed methodology is trained and tested on the publicly available CHB-MIT dataset while achieving 99.34% sensitivity, 98.67% specificity, and a false positive alarm rate of 0.039.DiscussionThe proposed method not only outperforms the existing methods in terms of sensitivity and specificity but is also computationally efficient, making it suitable for real-time epileptic seizure prediction systems. |
| format | Article |
| id | doaj-art-5708608b92c4480b95ef659d75eead2c |
| institution | Kabale University |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-5708608b92c4480b95ef659d75eead2c2025-08-20T03:42:44ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-08-011210.3389/fmed.2025.15668701566870Feature fusion ensemble classification approach for epileptic seizure prediction using electroencephalographic bio-signalsYazeed Alkhrijah0Yazeed Alkhrijah1Shehzad Khalid2Shehzad Khalid3Syed Muhammad Usman4Amina Jameel5Muhammad Zubair6Haya Aldossary7Aamir Anwar8Saad Arif9Department of Electrical Engineering, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi ArabiaKing Salman Center for Disability Research (KSCDR), Riyadh, Saudi ArabiaComputer and Information Sciences Research Center (CISRC), Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi ArabiaDepartment of Computer Engineering, Bahria University, Islamabad, PakistanDepartment of Computer Science, Bahria University, Islamabad, PakistanDepartment of Computer Engineering, Bahria University, Islamabad, PakistanInterdisciplinary Research Center for Finance and Digital Economy, King Fahd University of Petroleum and Minerals, Dhahran, Saudi ArabiaComputer Science Department, College of Science and Humanities, Imam Abdulrahman Bin Faisal University, Jubail, Saudi ArabiaSchool of Computing, University of Portsmouth, London Campus, London, United KingdomDepartment of Mechanical Engineering, College of Engineering, King Faisal University, Al Ahsa, Saudi ArabiaIntroductionEpilepsy is a neurological disorder in which patients experience recurrent seizures, with the frequency of occurrence more than twice a day, which highly affects a patient's life. In recent years, multiple researchers have proposed multiple machine learning and deep learning-based methods to predict the onset of seizures using electroencephalogram (EEG) signals before they occur; however, robust preprocessing to mitigate the effect of noise, channel selection to reduce dimensionality, and feature extraction remain challenges in accurate prediction.MethodsThis study proposes a novel method for accurately predicting epileptic seizures. In the first step, a Butterworth filter is applied, followed by a wavelet and a Fourier transform for the denoising of EEG signals. A non-overlapping window of 15 s is selected to segment the EEG signals, and an optimal spatial filter is applied to reduce the dimensionality. Handcrafted features, including both time and frequency domains, have been extracted and concatenated with the customized one-dimensional convolutional neural network-based features to form a comprehensive feature vector. It is then fed into three classifiers, including support vector machines, random forest, and long short-term memory (LSTM) units. The output of these classifiers is then fed into the model-agnostic meta learner ensemble classifier with LSTM as the base classifier for the final prediction of interictal and preictal states.ResultsThe proposed methodology is trained and tested on the publicly available CHB-MIT dataset while achieving 99.34% sensitivity, 98.67% specificity, and a false positive alarm rate of 0.039.DiscussionThe proposed method not only outperforms the existing methods in terms of sensitivity and specificity but is also computationally efficient, making it suitable for real-time epileptic seizure prediction systems.https://www.frontiersin.org/articles/10.3389/fmed.2025.1566870/fullAI in healthcareepilepsyelectroencephalogramepileptic seizure predictionsignal quality indexoptimal spatial filter |
| spellingShingle | Yazeed Alkhrijah Yazeed Alkhrijah Shehzad Khalid Shehzad Khalid Syed Muhammad Usman Amina Jameel Muhammad Zubair Haya Aldossary Aamir Anwar Saad Arif Feature fusion ensemble classification approach for epileptic seizure prediction using electroencephalographic bio-signals Frontiers in Medicine AI in healthcare epilepsy electroencephalogram epileptic seizure prediction signal quality index optimal spatial filter |
| title | Feature fusion ensemble classification approach for epileptic seizure prediction using electroencephalographic bio-signals |
| title_full | Feature fusion ensemble classification approach for epileptic seizure prediction using electroencephalographic bio-signals |
| title_fullStr | Feature fusion ensemble classification approach for epileptic seizure prediction using electroencephalographic bio-signals |
| title_full_unstemmed | Feature fusion ensemble classification approach for epileptic seizure prediction using electroencephalographic bio-signals |
| title_short | Feature fusion ensemble classification approach for epileptic seizure prediction using electroencephalographic bio-signals |
| title_sort | feature fusion ensemble classification approach for epileptic seizure prediction using electroencephalographic bio signals |
| topic | AI in healthcare epilepsy electroencephalogram epileptic seizure prediction signal quality index optimal spatial filter |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1566870/full |
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