Early Warning Prediction with Automatic Labeling in Epilepsy Patients

TL;DR

This paper introduces a meta learning framework for epilepsy seizure prediction that automatically labels noisy EEG data and improves early warning accuracy, enhancing patient safety.

Contribution

It presents a novel bi-level optimization approach that automatically labels data and optimizes model training, outperforming traditional methods in seizure prediction.

Findings

Meta learning significantly improves ictal prediction accuracy.

The framework captures intrinsic patterns in noisy data.

Predicted probabilities serve as effective early warning indicators.

Abstract

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.