Early warning for epilepsy patients is crucial for their safety and well-being, in terms of preventing or minimizing the severity of seizures. Through the patients' EEG data, we propose a meta learning framework for improving prediction on early ictal signals. To better utilize the meta label corrector method, we fuse the information from both the real data and the augmented data from the latent Stochastic differential equation(SDE). Besides, we also optimally select the latent dynamical system via distribution of transition time between real data and that from the latent SDE. In this way, the extracted tipping dynamical feature is also integrated into the meta network to better label the noisy data. To validate our method, LSTM is implemented as the baseline model. We conduct a series of experiments to predict seizure in various long-term window from 1-2 seconds input data and find surprisingly increment of prediction accuracy.

通过患者的脑电图数据，我们提出了一种基于元学习框架的早期癫痫信号预测方法，通过融合真实数据和隐式随机微分方程（SDE）生成的增广数据，以及优化选择潜在动力系统的转换时间分布，将提取的关键动力特征与元网络整合，以更好地标记嘈杂数据，并使用LSTM作为基准模型进行验证。我们进行了一系列实验，预测不同长期窗口（1-2秒的输入数据），发现预测准确性有意想不到的增加。

基于保稳转折潜在随机动态系统和元标签修正的早期预警