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arXiv · 2603.26821

Epileptic Seizure Prediction Using Patient-Adaptive Transformer Networks

Mohamed Mahdi·Asma Baghdadi
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Abstract

Epileptic seizure prediction from electroencephalographic (EEG) recordings remains challenging due to strong inter-patient variability and the complex temporal structure of neural signals. This paper presents a patient-adaptive transformer framework for short-horizon seizure forecasting. The proposed approach employs a two-stage training strategy: self-supervised pretraining is first used to learn general EEG temporal representations through autoregressive sequence modeling, followed by patient-specific fine-tuning for binary prediction of seizure onset within a 30-second horizon. To enable transformer-based sequence learning, multichannel EEG signals are processed using noise-aware preprocessing and discretized into tokenized temporal sequences. Experiments conducted on subjects from the TUH EEG dataset demonstrate that the proposed method achieves validation accuracies above 90% and F1 scores exceeding 0.80 across evaluated patients, supporting the effectiveness of combining self-supervised representation learning with patient-specific adaptation for individualized seizure prediction.

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Mohamed Mahdi, Asma Baghdadi. 2026-03-26. Epileptic Seizure Prediction Using Patient-Adaptive Transformer Networks. https://arxiv.org/abs/2603.26821

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