用Transformer模型预测癫痫发作风险，提升长期预报准确率。
SeizureFormer: A Transformer Model for IEA-Based Seizure Risk Forecasting
- 基于间期痫样放电和RNS系统数据，结合CNN与自注意力机制建模
- 1-14天预测窗口下平均ROC AUC达79.44%，PR AUC达76.29%
- 适用于个性化癫痫管理，尤其适合处理数据不平衡场景
我们提出SeizureFormer，一种基于Transformer的长周期癫痫发作风险预测模型，利用响应性神经刺激（RNS）系统中的间期痫样放电（IEA）替代生物标志物和长片段（LE）生物标志物。与依赖原始头皮脑电图的模型不同，SeizureFormer采用结构化、临床相关特征，并融合卷积神经网络补丁嵌入、多头自注意力与挤压-激励模块，以捕捉短期动态与长期发作周期。在五名患者及多个预测窗口（1至14天）上测试，其平均ROC AUC为79.44%，平均PR AUC为76.29%，优于统计、机器学习与深度学习基线模型。该模型在类别不平衡条件下表现出更强泛化能力，支持可解释且鲁棒的癫痫风险预测工具未来临床应用。
原文摘要 · Abstract (English)
We present SeizureFormer, a Transformer-based model for long-term seizure risk forecasting using interictal epileptiform activity (IEA) surrogate biomarkers and long episode (LE) biomarkers from responsive neurostimulation (RNS) systems. Unlike raw scalp EEG-based models, SeizureFormer leverages structured, clinically relevant features and integrates CNN-based patch embedding, multi-head self-attention, and squeeze-and-excitation blocks to model both short-term dynamics and long-term seizure cycles. Tested across five patients and multiple prediction windows (1 to 14 days), SeizureFormer achieved state-of-the-art performance with mean ROC AUC of 79.44 percent and mean PR AUC of 76.29 percent. Compared to statistical, machine learning, and deep learning baselines, it demonstrates enhanced generalizability and seizure risk forecasting performance under class imbalance. This work supports future clinical integration of interpretable and robust seizure forecasting tools for personalized epilepsy management.
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