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...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
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| Series: | Bioengineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2306-5354/12/2/109 |
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| Summary: | 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. |
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| ISSN: | 2306-5354 |