Predicting Epileptic Seizures Using EfficientNet-B0 and SVMs: A Deep Learning Methodology for EEG Analysis
Seizure prediction is a critical challenge in epilepsy management, offering the potential to improve patient outcomes through timely interventions. This study proposes a novel framework combining a convolutional neural network (CNN) based on EfficientNet-B0 and an ensemble of six Support Vector Mach...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Bioengineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2306-5354/12/2/109 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850081699932667904 |
|---|---|
| author | Yousif A. Saadoon Mohamad Khalil Dalia Battikh |
| author_facet | Yousif A. Saadoon Mohamad Khalil Dalia Battikh |
| author_sort | Yousif A. Saadoon |
| collection | DOAJ |
| description | Seizure prediction is a critical challenge in epilepsy management, offering the potential to improve patient outcomes through timely interventions. This study proposes a novel framework combining a convolutional neural network (CNN) based on EfficientNet-B0 and an ensemble of six Support Vector Machines (SVMs) with a voting mechanism for robust seizure prediction. The framework leverages normalized Short-Time Fourier Transform (STFT) and channel correlation features extracted from EEG signals to capture both spectral and spatial information. The methodology was validated on the CHB-MIT dataset across preictal windows of 10, 20, and 30 min, achieving accuracies of 96.12%, 94.89%, and 94.21%, and sensitivities of 95.21%, 93.98%, and 93.55%, respectively. Comparing the results with state-of-the-art methods, we highlight the framework’s robustness and adaptability. The EfficientNet-B0 backbone ensures high accuracy with computational efficiency, while the SVM ensemble enhances prediction reliability by mitigating noise and variability in EEG data. |
| format | Article |
| id | doaj-art-ce76e680f72e49baac1ab5519d8cdd5b |
| institution | DOAJ |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-ce76e680f72e49baac1ab5519d8cdd5b2025-08-20T02:44:40ZengMDPI AGBioengineering2306-53542025-01-0112210910.3390/bioengineering12020109Predicting Epileptic Seizures Using EfficientNet-B0 and SVMs: A Deep Learning Methodology for EEG AnalysisYousif A. Saadoon0Mohamad Khalil1Dalia Battikh2Doctoral School of Science and Technology, Lebanese University, Hadath Campus, Beirut 1003, LebanonCollege of Engineering, Lebanese University, Tripoli 1300, LebanonCollege of Engineering, Lebanese University, Tripoli 1300, LebanonSeizure prediction is a critical challenge in epilepsy management, offering the potential to improve patient outcomes through timely interventions. This study proposes a novel framework combining a convolutional neural network (CNN) based on EfficientNet-B0 and an ensemble of six Support Vector Machines (SVMs) with a voting mechanism for robust seizure prediction. The framework leverages normalized Short-Time Fourier Transform (STFT) and channel correlation features extracted from EEG signals to capture both spectral and spatial information. The methodology was validated on the CHB-MIT dataset across preictal windows of 10, 20, and 30 min, achieving accuracies of 96.12%, 94.89%, and 94.21%, and sensitivities of 95.21%, 93.98%, and 93.55%, respectively. Comparing the results with state-of-the-art methods, we highlight the framework’s robustness and adaptability. The EfficientNet-B0 backbone ensures high accuracy with computational efficiency, while the SVM ensemble enhances prediction reliability by mitigating noise and variability in EEG data.https://www.mdpi.com/2306-5354/12/2/109deep learningseizure predictionEEGclassification |
| spellingShingle | Yousif A. Saadoon Mohamad Khalil Dalia Battikh Predicting Epileptic Seizures Using EfficientNet-B0 and SVMs: A Deep Learning Methodology for EEG Analysis Bioengineering deep learning seizure prediction EEG classification |
| title | Predicting Epileptic Seizures Using EfficientNet-B0 and SVMs: A Deep Learning Methodology for EEG Analysis |
| title_full | Predicting Epileptic Seizures Using EfficientNet-B0 and SVMs: A Deep Learning Methodology for EEG Analysis |
| title_fullStr | Predicting Epileptic Seizures Using EfficientNet-B0 and SVMs: A Deep Learning Methodology for EEG Analysis |
| title_full_unstemmed | Predicting Epileptic Seizures Using EfficientNet-B0 and SVMs: A Deep Learning Methodology for EEG Analysis |
| title_short | Predicting Epileptic Seizures Using EfficientNet-B0 and SVMs: A Deep Learning Methodology for EEG Analysis |
| title_sort | predicting epileptic seizures using efficientnet b0 and svms a deep learning methodology for eeg analysis |
| topic | deep learning seizure prediction EEG classification |
| url | https://www.mdpi.com/2306-5354/12/2/109 |
| work_keys_str_mv | AT yousifasaadoon predictingepilepticseizuresusingefficientnetb0andsvmsadeeplearningmethodologyforeeganalysis AT mohamadkhalil predictingepilepticseizuresusingefficientnetb0andsvmsadeeplearningmethodologyforeeganalysis AT daliabattikh predictingepilepticseizuresusingefficientnetb0andsvmsadeeplearningmethodologyforeeganalysis |