# Early Warning Prediction with Automatic Labeling in Epilepsy Patients

arXiv: https://arxiv.org/abs/2310.06059 · HTML: https://mlsanity.com/paper/2310.06059/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 58 references that are in the map.
- First posted on arXiv 9 Oct 2023.

## Builds on (most cited first, 12 of 12)

- [Deep learning-based electroencephalography analysis: a systematic review](https://mlsanity.com/paper/1901.05498/) (2019)
- [Learning From Noisy Large-Scale Datasets With Minimal Supervision](https://mlsanity.com/paper/1701.01619/) (2017)
- [Synthetic Data for Deep Learning](https://mlsanity.com/paper/1909.11512/) (2019)
- [Meta Label Correction for Noisy Label Learning](https://mlsanity.com/paper/1911.03809/) (2019)
- [Learning from Untrusted Data](https://mlsanity.com/paper/1611.02315/) (2016)
- [EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals](https://mlsanity.com/paper/1806.01875/) (2018)
- [Focal onset seizure prediction using convolutional networks](https://mlsanity.com/paper/1805.11576/) (2018)
- [Deep Reinforcement Learning in Finite-Horizon to Explore the Most Probable Transition Pathway](https://mlsanity.com/paper/2304.12994/) (2023)
- [An Optimal Control Method to Compute the Most Likely Transition Path for Stochastic Dynamical Systems with Jumps](https://mlsanity.com/paper/2203.16874/) (2022)
- [A Generalised Seizure Prediction with Convolutional Neural Networks for Intracranial and Scalp Electroencephalogram Data Analysis](https://mlsanity.com/paper/1707.01976/) (2017)
- [Analyzing EEG Data with Machine and Deep Learning: A Benchmark](https://mlsanity.com/paper/2203.10009/) (2022)
- [Multi-task Meta Label Correction for Time Series Prediction](https://mlsanity.com/paper/2303.08103/) (2023)

## Built on by (most cited first, 2 of 2)

- [Early warning prediction: Onsager-Machlup vs Schr"odinger](https://mlsanity.com/paper/2602.00143/) (2026)
- [Beyond Distance: Quantifying Point Cloud Dynamics with Persistent Homology and Dynamic Optimal Transport](https://mlsanity.com/paper/2603.15683/) (2026)

## Abstract (arXiv)

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.