利用患者自适应变换器网络进行癫痫发作预测
Epileptic Seizure Prediction Using Patient-Adaptive Transformer Networks
AI总结：
本文提出一种患者自适应变换器框架，通过自监督预训练和患者特定微调实现短时间尺度癫痫发作预测，实验表明方法在TUH EEG数据集上验证准确率超过90%。
摘要：
从脑电图（EEG）记录中预测癫痫发作仍具有挑战性，因为存在强烈的患者间差异性和神经信号的复杂时间结构。本文提出了一种患者自适应变换器框架用于短时间尺度的发作预测。所提出的方法采用两阶段训练策略：首先使用自监督预训练学习通用的EEG时间表示，通过自回归序列建模；随后进行患者特定的微调，以在30秒范围内进行二元预测。为了实现基于变换器的序列学习，多通道EEG信号通过噪声感知预处理并离散化为分隔的时间序列。在TUH EEG数据集上的实验表明，所提出的方法在评估患者中实现了验证准确率超过90%和F1分数超过0.80，支持了将自监督表示学习与患者特定适应相结合用于个性化发作预测的有效性。
英文摘要：
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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