# SeizureFormer: A Transformer Model for IEA-Based Seizure Risk Forecasting

arXiv: https://arxiv.org/abs/2504.16098 · HTML: https://mlsanity.com/paper/2504.16098/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 32 references that are in the map.
- First posted on arXiv 13 Apr 2025.

## Builds on (most cited first, 4 of 4)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review](https://mlsanity.com/paper/2002.01925/) (2020)
- [Learning Robust Features using Deep Learning for Automatic Seizure Detection](https://mlsanity.com/paper/1608.00220/) (2016)
- [TimeDistill: Efficient Long-Term Time Series Forecasting with MLP via Cross-Architecture Distillation](https://mlsanity.com/paper/2502.15016/) (2025)

## Built on by (most cited first, 1 of 1)

- [PPG-Distill: Efficient Photoplethysmography Signals Analysis via Foundation Model Distillation](https://mlsanity.com/paper/2509.19215/) (2025)

## Abstract (arXiv)

We present SeizureFormer, a Transformer-based model for long-term seizure risk forecasting using interictal epileptiform activity (IEA) surrogate biomarkers and long episode (LE) biomarkers from responsive neurostimulation (RNS) systems. Unlike raw scalp EEG-based models, SeizureFormer leverages structured, clinically relevant features and integrates CNN-based patch embedding, multi-head self-attention, and squeeze-and-excitation blocks to model both short-term dynamics and long-term seizure cycles. Tested across five patients and multiple prediction windows (1 to 14 days), SeizureFormer achieved state-of-the-art performance with mean ROC AUC of 79.44 percent and mean PR AUC of 76.29 percent. Compared to statistical, machine learning, and deep learning baselines, it demonstrates enhanced generalizability and seizure risk forecasting performance under class imbalance. This work supports future clinical integration of interpretable and robust seizure forecasting tools for personalized epilepsy management.