Attentive CNN EEG or ACE-SeizNet: An Attention-Enhanced CNN Model for Automated EEG-Based Seizure Detection through Multi-Domain Deep Feature Fusion

Background Epileptic seizure detection using electroencephalogram (EEG) signals is crucial for automated diagnosis and monitoring of neurological disorders. Traditional machine learning and deep learning models often face challenges in capturing subtle seizure patterns and maintaining high generalization across datasets. To address these limitations, this study proposes ACE-SeizNet, a CNN-based attention-enhanced architecture for seizure detection. Method ACE-SeizNet integrates hierarchical feature extraction using convolutional layers, dimensionality reduction via pooling layers, and attention mechanisms for feature refinement. The model is trained and evaluated on EEG datasets using performance metrics such as accuracy, precision, recall, F1-score, Jaccard similarity, sensitivity, specificity, and AUC-ROC. Result ACE-SeizNet achieves an accuracy of 98.89%, sensitivity of 98.77%, specificity of 98.59%, and an F1-score of 99.67%, surpassing state-of-the-art models in seizure classification. The high AUC-ROC (0.999885 on the training set) validates its robustness in distinguishing seizure from non-seizure states. The model also demonstrates consistent performance across training, validation, and test datasets, confirming its reliability for clinical applications. Conclusion ACE-SeizNet provides an efficient and accurate solution for EEG-based seizure detection, offering superior performance over existing methods. Its ability to generalize across datasets and effectively detect seizures across multiple EEG channels makes it a promising tool for real-time clinical deployment. Future work will focus on further optimizing computational efficiency and enhancing interpretability for clinical usability.