用自监督+个性化微调，提升癫痫发作预测准确率。
Epileptic Seizure Prediction Using Patient-Adaptive Transformer Networks
- 先自监督学通用脑电特征，再针对个体微调预测
- 30秒内预测发作，准确率超90%，F1超0.80
- 适合需要个性化癫痫预警的临床研究
从脑电图（EEG）预测癫痫发作仍面临患者间差异大、神经信号时序复杂等挑战。本文提出一种患者自适应的Transformer框架，用于短时程发作预测。方法采用两阶段训练：首先通过自回归序列建模进行自监督预训练，学习通用的脑电信号时序表征；随后在个体层面进行微调，实现30秒内发作前兆的二分类预测。为支持基于Transformer的序列学习，多通道脑电信号经抗噪预处理并离散化为标记序列。在TUH EEG数据集上的实验表明，该方法在所有被评估患者中验证准确率均超过90%，F1分数高于0.80，证明结合自监督表示学习与患者特异性适配对个体化发作预测有效。
原文摘要 · Abstract (English)
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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