ArticleBrain informatics2024
HyEpiSeiD: a hybrid convolutional neural network and gated recurrent unit model for epileptic seizure detection from electroencephalogram signals.
Article in Brain informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Quantum inspired feature engineering for explainable EEG signal classification.Scientific reports · 2026Article
- Hybrid fuzzy machine learning models optimized with meta-heuristics for accurate EEG-based neurological assessment.Scientific reports · 2026Article
- Boosting brain tumor detection with an optimized ResNet and explainability via Grad-CAM and LIME.Brain informatics · 2025Article
- Seizure Onset Zone Detection Based on Convolutional Neural Networks and EEG Signals.Brain sciences · 2024Article
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3 authors.
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Abstract
Epileptic seizure (ES) detection is an active research area, that aims at patient-specific ES detection with high accuracy from electroencephalogram (EEG) signals. The early detection of seizure is crucial for timely medical intervention and prevention of further injuries of the patients. This work proposes a robust deep learning framework called HyEpiSeiD that extracts self-trained features from the pre-processed EEG signals using a hybrid combination of convolutional neural network followed by two gated recurrent unit layers and performs prediction based on those extracted features. The proposed HyEpiSeiD framework is evaluated on two public datasets, the UCI Epilepsy and Mendeley datasets. The proposed HyEpiSeiD model achieved 99.01% and 97.50% classification accuracy, respectively, outperforming most of the state-of-the-art methods in epilepsy detection domain.
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