Early warning for epilepsy patients is crucial for their safety and well-being, in particular to prevent or minimize the severity of seizures. Through the patients' EEG data, we propose a meta learning framework to improve the prediction of early ictal signals. The proposed bi-level optimization framework can help automatically label noisy data at the early ictal stage, as well as optimize the training accuracy of the backbone model. To validate our approach, we conduct a series of experiments to predict seizure onset in various long-term windows, with LSTM and ResNet implemented as the baseline models. Our study demonstrates that not only the ictal prediction accuracy obtained by meta learning is significantly improved, but also the resulting model captures some intrinsic patterns of the noisy data that a single backbone model could not learn. As a result, the predicted probability generated by the meta network serves as a highly effective early warning indicator.

翻译：对于癫痫患者而言，早期预警对其安全与健康至关重要，特别是能够预防或减轻癫痫发作的严重程度。基于患者的脑电图数据，我们提出了一种元学习框架，用于改善发作前期信号的预测性能。该双层优化框架能够自动标注发作早期阶段的噪声数据，同时优化骨干模型的训练精度。为验证本方法，我们采用LSTM和ResNet作为基线模型，开展了一系列针对不同时长窗口内癫痫发作起始的预测实验。研究表明，元学习不仅显著提升了发作预测准确率，还使模型捕捉到了单一骨干模型无法学习的噪声数据内在模式。由此，元网络生成的预测概率可作为高效的早期预警指标。