# Focal onset seizure prediction using convolutional networks

arXiv: https://arxiv.org/abs/1805.11576 · HTML: https://mlsanity.com/paper/1805.11576/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2018.

## Built on by (most cited first, 18 of 18)

- [Data Augmentation for Seizure Prediction with Generative Diffusion Model](https://mlsanity.com/paper/2306.08256/) (2023)
- [EvoBrain: Dynamic Multi-Channel EEG Graph Modeling for Time-Evolving Brain Networks](https://mlsanity.com/paper/2509.15857/) (2025)
- [An End-to-End Deep Learning Approach for Epileptic Seizure Prediction](https://mlsanity.com/paper/2108.07453/) (2021)
- [Epilepsy Seizure Detection and Prediction using an Approximate Spiking Convolutional Transformer](https://mlsanity.com/paper/2402.09424/) (2024)
- [Long-Term EEG Partitioning for Seizure Onset Detection](https://mlsanity.com/paper/2412.15598/) (2024)
- [Multichannel Synthetic Preictal EEG Signals to Enhance the Prediction of Epileptic Seizures](https://mlsanity.com/paper/2205.03239/) (2022)
- [Binary Single-dimensional Convolutional Neural Network for Seizure Prediction](https://mlsanity.com/paper/2206.07518/) (2022)
- [An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works](https://mlsanity.com/paper/2105.14278/) (2021)
- [Random Forest classifier for EEG-based seizure prediction](https://mlsanity.com/paper/2106.04510/) (2021)
- [Supervised and Unsupervised Deep Learning Approaches for EEG Seizure Prediction](https://mlsanity.com/paper/2304.14922/) (2023)
- [Early Warning Prediction with Automatic Labeling in Epilepsy Patients](https://mlsanity.com/paper/2310.06059/) (2023)
- [Preictal Period Optimization for Deep Learning-Based Epileptic Seizure Prediction](https://mlsanity.com/paper/2407.14876/) (2024)
- [Automatic Seizure Prediction using CNN and LSTM](https://mlsanity.com/paper/2211.02679/) (2022)
- [Time Scale Network: A Shallow Neural Network For Time Series Data](https://mlsanity.com/paper/2311.06170/) (2023)
- [Federated Learning for Epileptic Seizure Prediction Across Heterogeneous EEG Datasets](https://mlsanity.com/paper/2508.08159/) (2025)
- [Personalization on a Budget: Minimally-Labeled Continual Learning for Resource-Efficient Seizure Detection](https://mlsanity.com/paper/2509.13974/) (2025)
- [CLSP-REQA: A Real-Time Quality-Aware Closed-Loop Seizure Prediction Framework with Mamba-BiLSTM and Confidence-Gated Intervention](https://mlsanity.com/paper/2606.00074/) (2026)
- [CG-MambaNet: A spatiotemporal framework for cross-patient epileptic seizure prediction using CNN-GCN-Mamba-BiLSTM with event-level clinical evaluation](https://mlsanity.com/paper/2606.08226/) (2026)

## Abstract (arXiv)

Objective: This work investigates the hypothesis that focal seizures can be predicted using scalp electroencephalogram (EEG) data. Our first aim is to learn features that distinguish between the interictal and preictal regions. The second aim is to define a prediction horizon in which the prediction is as accurate and as early as possible, clearly two competing objectives. Methods: Convolutional filters on the wavelet transformation of the EEG signal are used to define and learn quantitative signatures for each period: interictal, preictal, and ictal. The optimal seizure prediction horizon is also learned from the data as opposed to making an a priori assumption. Results: Computational solutions to the optimization problem indicate a ten-minute seizure prediction horizon. This result is verified by measuring Kullback-Leibler divergence on the distributions of the automatically extracted features. Conclusion: The results on the EEG database of 204 recordings demonstrate that (i) the preictal phase transition occurs approximately ten minutes before seizure onset, and (ii) the prediction results on the test set are promising, with a sensitivity of 87.8% and a low false prediction rate of 0.142 FP/h. Our results significantly outperform a random predictor and other seizure prediction algorithms. Significance: We demonstrate that a robust set of features can be learned from scalp EEG that characterize the preictal state of focal seizures.