A Deep Hybrid CSAE-GRU Framework with Two-Stage Balancing for Automatic Epileptic Seizure Detection Using EEG-Derived Features

Fei Xiang¹Mingyue Liu¹Wenna Chen^2*Shaojie Zheng¹Jincan Zhang¹Ganqin Du²

• ¹Henan University of Science and Technology, Luoyang, China
• ²The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China

Abstract Introduction: Epilepsy is a neurological disorder characterized by abnormal neuronal discharges in the brain, posing a persistent challenge in clinical diagnosis. This study presents a high-performance epileptic seizure detection framework that integrates advanced feature extraction and classification techniques using EEG signals. Methods: We conduct experiments on the Bonn and CHB-MIT EEG datasets. The EEG signals are preprocessed through bandpass filtering and five-level Discrete Wavelet Transform (DWT) decomposition. From each sub-band, four representative features are systematically extracted. To mitigate severe class imbalance, we propose a two-stage balancing strategy: cluster centroid-based under-sampling initially reduces the interictal-to-ictal ratio to 2:1, followed by Borderline Synthetic Minority Oversampling Technique (BLSMOTE) in the feature space. A hybrid classification model that combines Convolutional Sparse Autoencoder (CSAE) with Gated Recurrent Unit (GRU) is proposed in the paper. The encoder weights from the pre-trained CSAE are transferred to the GRU-based classifier to enhance feature representation and model generalization. Results: The proposed method achieves outstanding performance, with accuracy, sensitivity, specificity, precision, f1 score and AUC of 98.46%, 98.27%, 98.50%, 98.36%, 98.31% and 98.23% on the Bonn dataset, and 99.49%, 99.21%, 99.77%, 99.49%, 99.35% and 99.57% on the CHB-MIT dataset, respectively. These results validate the effectiveness of the proposed approach. Discussion: This study introduces a novel framework combining cluster centroid-based under-sampling, BLSMOTE oversampling, and transfer learning via CSAE-GRU integration. The method offers a promising direction for reliable and clinically applicable automated epilepsy diagnosis.