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<p>Epileptic seizures are serious neurological events that significantly affect patients’ health and quality of life. Accurate seizure prediction is essential for enabling early intervention and improving clinical outcomes. Most existing prediction systems are patient-specific, requiring large amounts of individualized data and exhibiting poor generalizability across subjects. A major challenge in this field lies in the limited availability of preictal EEG segments, recorded shortly before a seizure onset, compared to the more abundant interictal segments, which represent normal brain activity between seizures. This study proposes a hybrid deep learning architecture for patient-independent epileptic seizure identification. The framework is designed to perform robustly across multiple patients without the need for subject-specific calibration. A data augmentation technique based on a random walk algorithm was adopted from the literature to address the scarcity of preictal EEG segments. Then the power spectral density (PSD) was used to extract features, capturing important frequency-domain characteristics of brain activity. The proposed hybrid architecture integrates a Hierarchical Temporal Separable Convolutional Network (HTSCN), a Dual-Stage Bidirectional Recurrent Neural Network (DS-Bi-RNN), and a Multi-Head Attention Mechanism, enabling effective extraction of spatial, temporal, and contextual features from non-stationary EEG signals while addressing inter-patient variability. Experimental evaluations conducted on the CHB-MIT and Siena datasets demonstrated the strong discriminative performance of the proposed model in classifying preictal and interictal EEG states, achieving test accuracies of 98.74% and 97.35%, respectively. These results highlight the robustness and generalization capability of the architecture, establishing it as a promising approach for scalable and clinically applicable epileptic seizure prediction systems, though further validation on continuous EEG streams is warranted to fully characterize alarm rates in real-world deployment.</p>

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Hierarchical temporal-separable convolutional-recurrent attention network for EEG-based epileptic seizure prediction

  • 期刊论文

  • 发表日期：

    2026年7月21日

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  • Khadija Slama,
  • Ali Yahyaouy,
  • Jamal Riffi,
  • Mohamed Adnane Mahraz,
  • Hamid Tairi

摘要

Epileptic seizures are serious neurological events that significantly affect patients’ health and quality of life. Accurate seizure prediction is essential for enabling early intervention and improving clinical outcomes. Most existing prediction systems are patient-specific, requiring large amounts of individualized data and exhibiting poor generalizability across subjects. A major challenge in this field lies in the limited availability of preictal EEG segments, recorded shortly before a seizure onset, compared to the more abundant interictal segments, which represent normal brain activity between seizures. This study proposes a hybrid deep learning architecture for patient-independent epileptic seizure identification. The framework is designed to perform robustly across multiple patients without the need for subject-specific calibration. A data augmentation technique based on a random walk algorithm was adopted from the literature to address the scarcity of preictal EEG segments. Then the power spectral density (PSD) was used to extract features, capturing important frequency-domain characteristics of brain activity. The proposed hybrid architecture integrates a Hierarchical Temporal Separable Convolutional Network (HTSCN), a Dual-Stage Bidirectional Recurrent Neural Network (DS-Bi-RNN), and a Multi-Head Attention Mechanism, enabling effective extraction of spatial, temporal, and contextual features from non-stationary EEG signals while addressing inter-patient variability. Experimental evaluations conducted on the CHB-MIT and Siena datasets demonstrated the strong discriminative performance of the proposed model in classifying preictal and interictal EEG states, achieving test accuracies of 98.74% and 97.35%, respectively. These results highlight the robustness and generalization capability of the architecture, establishing it as a promising approach for scalable and clinically applicable epileptic seizure prediction systems, though further validation on continuous EEG streams is warranted to fully characterize alarm rates in real-world deployment.