Epileptic Seizure Prediction Using Patient-Adaptive Transformer Networks

• URL: http://arxiv.org/abs/2603.26821v1
• Date: Thu, 26 Mar 2026 23:48:10 GMT
• Title: Epileptic Seizure Prediction Using Patient-Adaptive Transformer Networks
• Abstract summary: This paper presents a patient-adaptive transformer framework for short-horizon seizure forecasting.<n>The proposed approach employs a two-stage training strategy: self-supervised pretraining is first used to learn general EEG temporal representations.<n>To enable transformer-based sequence learning, multichannel EEG signals are processed using noise-aware preprocessing.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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