  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.
