en ● Artificial Intelligence مقاله اصيل پژوهشي Orginal Article <strong>Background</strong>: Epilepsy is a chronic neurological disorder marked by recurrent daily seizures that threaten patient safety. Electroencephalography (EEG) is a crucial neuroimaging tool for epilepsy diagnosis, but manual interpretation of EEG signals is challenging for clinicians. To assist specialists, automated systems, such as computer-aided diagnosis systems (CADS) based on deep learning (DL) are essential.&nbsp;<br> <strong>Methods</strong>: The proposed CADS system was validated using the Turkish epilepsy dataset. In preprocessing, EEG signals were filtered, down-sampled, re-referenced using common average reference (CAR), and segmented into multiple temporal windows. A new feature extraction framework combining one-dimensional convolutional neural networks (1D-CNN), bidirectional long short-term memory (Bi-LSTM), and an attention mechanism was developed. All experiments were performed using 5-fold cross-validation. Post-hoc explainability was evaluated using explainable artificial intelligence (XAI) techniques, including t-distributed stochastic neighbor embedding (t-SNE) and shapley additive explanations (SHAP).<br> <strong>Results</strong>: The proposed CADS achieved a seizure diagnosis accuracy of 99.49%, demonstrating high robustness across the validation folds, with minimal variance between folds (&plusmn;0.12%). Feature space visualization confirmed clear class separation, and SHAP analysis provided clinically meaningful explanations for model decisions.<br> <strong>Conclusion</strong>: The proposed DL architecture shows strong potential for reliable and interpretable automatic epileptic seizure detection from EEG. This CADS can significantly reduce the diagnostic burden on clinicians and support real-time decision-making in clinical environments.<br> &nbsp; Epileptic seizures, EEG signals, Deep learning (DL), Attention mechanism, t-SNE, SHAP 779 792 http://jrh.gmu.ac.ir/browse.php?a_code=A-10-2892-1&slc_lang=en&sid=1 Mohammad Mehdi Barzegar barzegar.mehdi9877@gmail.com 100319475328460044502 0009-0002-7934-8204 No Department of Biomedical Engineering, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran. Nazila Ahmadi Daryakenari nazila.ahmadii@ut.ac.ir 100319475328460044503 0009-0002-9531-6341 No Department of Epileptology, University Hospital Bonn, University of Bonn, Bonn, Germany. Marjane Khodatars khodatars1marjane@gmail.com 100319475328460044504 100319475328460044504 Yes Department of Medical Engineering, MMS.C. Islamic Azad University, Mashhad, Iran.