Epileptic Seizure Prediction Using Patient-Adaptive Transformer Networks
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866914427252506624 |
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| author | Mahdi, Mohamed Baghdadi, Asma |
| author_facet | Mahdi, Mohamed Baghdadi, Asma |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_26821 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Epileptic Seizure Prediction Using Patient-Adaptive Transformer Networks Mahdi, Mohamed Baghdadi, Asma Machine Learning Artificial Intelligence 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. |
| title | Epileptic Seizure Prediction Using Patient-Adaptive Transformer Networks |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2603.26821 |