Systematic Evaluation of Deep Learning Architectures for Epilepsy Detection with Patient-Based Validation

EEG signal processing is crucial for diagnosing neurological diseases, such as epilepsy. This study systematically compares the performance of time-domain and time-frequency domain representations in detecting epilepsy seizures. The data were divided into four-second windows at a sampling frequency of 256 Hz, and data leakage was prevented by applying a patient-based discrimination protocol. The training, validation, and test sets were separated by patient IDs, and windows from the same patient were not included in more than one partition. Six deep learning models, including BiLSTM, 1D-CNN, STFT-CNN, STFT-CNN with attention mechanism, ResSTFT-CNN, and ResSTFT-CNN with attention mechanism, were evaluated under the same training conditions. AdamW optimization, label smoothing, and early stopping algorithms were applied to all models. The models were compared in terms of accuracy, F1-Macro, AUROC, AUPRC, calibration metrics, and inference rate. Results showed that the 1D-CNN model exhibited significant superiority. The model offered the most balanced performance with an accuracy of 0.957, an AUROC of 0.993, and an inference time per sample of 0.09 milliseconds. BiLSTM came in second with 0.936 accuracy. For STFT-based models, the addition of an attention mechanism significantly improved performance, with STFT-CNN accuracy increasing from 0.78 to 0.91. Model complexity analyses revealed that 1D-CNN strikes a balance between high performance and a low parameter count. Working in the time-domain representation, 1DCNN offers an optimal solution for epilepsy detection, striking a balance between accuracy, speed, and clinical applicability. The patient-based discrimination strategy allows for the realistic generalization capacity of the models to be evaluated.