Hybrid Attention-Based Transformers-CNN Model for Seizure Prediction Through Electronic Health Records

• Published in: International journal of advanced computer science & applications Vol. 16; no. 2
• Main Authors: Ramesh, Janjhyam Venkata Naga, Misba, M., Balaji, S., Kumar, K. Kiran, Muniyandy, Elangovan, El-Ebiary, Yousef A. Baker, Bala, B Kiran, Elbasir, Radwan Abdulhadi .M.
• Format: Journal Article
• Language: English
• Published: West Yorkshire Science and Information (SAI) Organization Limited 2025
• Subjects: Accuracy; Artificial neural networks; Big Data; Brain research; Childhood; Cognition & reasoning; Computer science; Convulsions & seizures; Data points; Datasets; Deep learning; Electroencephalography; Electronic health records; Engineering; Epilepsy; Feature extraction; Informatics; Machine learning; Massive data points; Methods; Neural networks; Neurological diseases; Patients; Seizures; Sensitivity; Spatiotemporal data
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2025.01602110

Seizures are a serious neurological disease, and proper prognosis by electroencephalography (EEG) dramatically enhances patient outcomes. Current seizure prediction methods fail to deal with big data and usually need intensive preprocessing. Recent breakthroughs in deep learning can automatically extract features and detect seizures. This work suggests a CNN-Transformer model for epileptic seizure prediction from EEG data with the goal of increasing precision and prediction rates by investigating spatial and temporal relationships within data. The innovation is in employing CNN for spatial feature extraction and a Transformer-based architecture for temporal dependencies over the long term. In contrast to conventional methods that depend on hand-crafted features, this method uses an optimization approach to enhance predictive performance for large-scale EEG datasets. The dataset, which was obtained from Kaggle, consists of EEG signals from 500 subjects with 4097 data points per subject in 23.6 seconds. CNN layers extract spatial characteristics, while the Transformer takes temporal sequences in through a Self-Attention Profiler to process EEG's temporality. The suggested CNN-Transformer model also performs well with 98.3% accuracy, 97.9% precision, 98.73% F1-score, 98.21% specificity, and 98.5% sensitivity. These outcomes show how the model identifies seizures while being low on false positives. The results indicate how the hybrid CNN-Transformer model is effective at utilizing spatiotemporal EEG features in seizure prediction. Its high sensitivity and accuracy indicate important clinical promise for early intervention, enhancing treatment for epilepsy patients. This method improves seizure prediction, allowing for better management and early therapeutic response in the clinic.