Explainable Epileptic Seizure Detection from Electroencephalography Signals via CNN–Bi-LSTM Attention Hybrid Model
Year: 2025
Type: article
Abstract: Background: 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.Methods: 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).Results: The proposed CADS achieved a seizure diagnosis accuracy of 99.49%, demonstrating high robustness across the validation folds, with minimal variance between folds (±0.12%).Feature space visualization confirmed clear class separation, and SHAP analysis provided clinically meaningful explanations for model decisions. Conclusion: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.
Source: Journal of Research and Health
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W7127198533