# Spatio-Temporal Attention Network for Epileptic Seizure Prediction

arXiv: https://arxiv.org/abs/2511.02846 · HTML: https://mlsanity.com/paper/2511.02846/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 19 references that are in the map.
- First posted on arXiv 24 Oct 2025.

## Builds on (most cited first, 1 of 1)

- [Seizure Detection and Prediction by Parallel Memristive Convolutional Neural Networks](https://mlsanity.com/paper/2206.09951/) (2022)

## Abstract (arXiv)

In this study, we present a deep learning framework that learns complex spatio-temporal correlation structures of EEG signals through a Spatio-Temporal Attention Network (STAN) for accurate predictions of onset of seizures for Epilepsy patients. Unlike existing methods, which rely on feature engineering and/or assume fixed preictal durations, our approach simultaneously models spatio-temporal correlations through STAN and employs an adversarial discriminator to distinguish preictal from interictal attention patterns, enabling patient-specific learning. Evaluation on CHB-MIT and MSSM datasets demonstrates 96.6% sensitivity with 0.011/h false detection rate on CHB-MIT, and 94.2% sensitivity with 0.063/h FDR on MSSM, significantly outperforming state-of-the-art methods. The framework reliably detects preictal states at least 15 minutes before an onset, with patient-specific windows extending to 45 minutes, providing sufficient intervention time for clinical applications.