# Preictal Period Optimization for Deep Learning-Based Epileptic Seizure Prediction

arXiv: https://arxiv.org/abs/2407.14876 · HTML: https://mlsanity.com/paper/2407.14876/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 66 references that are in the map.
- First posted on arXiv 20 Jul 2024.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [On the Convergence of Adam and Beyond](https://mlsanity.com/paper/1904.09237/) (2019)
- [Leakage and the Reproducibility Crisis in ML-based Science](https://mlsanity.com/paper/2207.07048/) (2022)
- [Focal onset seizure prediction using convolutional networks](https://mlsanity.com/paper/1805.11576/) (2018)
- [A Generative Model to Synthesize EEG Data for Epileptic Seizure Prediction](https://mlsanity.com/paper/2012.00430/) (2020)
- [Objective evaluation metrics for automatic classification of EEG events](https://mlsanity.com/paper/1712.10107/) (2017)
- [Epilepsy Seizure Detection and Prediction using an Approximate Spiking Convolutional Transformer](https://mlsanity.com/paper/2402.09424/) (2024)
- [Unveiling Intractable Epileptogenic Brain Networks with Deep Learning Algorithms: A Novel and Comprehensive Framework for Scalable Seizure Prediction with Unimodal Neuroimaging Data in Pediatric Patients](https://mlsanity.com/paper/2309.02580/) (2023)

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

- [EEG-based Epileptic Prediction via a Two-stage Channel-aware Set Transformer Network](https://mlsanity.com/paper/2507.15364/) (2025)
- [Automated Video-EEG Analysis in Epilepsy Studies: Advances and Challenges](https://mlsanity.com/paper/2503.19949/) (2025)

## Abstract (arXiv)

Accurate prediction of epileptic seizures could prove critical for improving patient safety and quality of life in drug-resistant epilepsy. Although deep learning-based approaches have shown promising seizure prediction performance using scalp electroencephalogram (EEG) signals, substantial limitations still impede their clinical adoption. Furthermore, identifying the optimal preictal period (OPP) for labeling EEG segments remains a challenge. Here, we not only develop a competitive deep learning model for seizure prediction but, more importantly, leverage it to demonstrate a methodology to comprehensively evaluate the predictive performance in the seizure prediction task. For this, we introduce a CNN-Transformer deep learning model to detect preictal spatiotemporal dynamics, alongside a novel Continuous Input-Output Performance Ratio (CIOPR) metric to determine the OPP. We trained and evaluated our model on 19 pediatric patients of the open-access CHB-MIT dataset in a subject-specific manner. Using the OPP of each patient, preictal and interictal segments were correctly identified with an average sensitivity of 99.31%, specificity of 95.34%, AUC of 99.35%, and F1- score of 97.46%, while prediction time averaged 76.8 minutes before onset. Notably, our novel CIOPR metric allowed outlining the impact of different preictal period definitions on prediction time, accuracy, output stability, and transition time between interictal and preictal states in a comprehensive and quantitative way and highlighted the importance of considering both inter- and intra-patient variability in seizure prediction.