# An End-to-End Deep Learning Approach for Epileptic Seizure Prediction

arXiv: https://arxiv.org/abs/2108.07453 · HTML: https://mlsanity.com/paper/2108.07453/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 40 references that are in the map.
- First posted on arXiv 17 Aug 2021.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Focal onset seizure prediction using convolutional networks](https://mlsanity.com/paper/1805.11576/) (2018)
- [Convolutional Neural Networks for Epileptic Seizure Prediction](https://mlsanity.com/paper/1811.00915/) (2018)
- [Seizure Detection using Least EEG Channels by Deep Convolutional Neural Network](https://mlsanity.com/paper/1901.05305/) (2019)
- [Human Intracranial EEG Quantitative Analysis and Automatic Feature Learning for Epileptic Seizure Prediction](https://mlsanity.com/paper/1904.03603/) (2019)

## Built on by (most cited first, 7 of 7)

- [Data Augmentation for Seizure Prediction with Generative Diffusion Model](https://mlsanity.com/paper/2306.08256/) (2023)
- [Multichannel Synthetic Preictal EEG Signals to Enhance the Prediction of Epileptic Seizures](https://mlsanity.com/paper/2205.03239/) (2022)
- [C2SP-Net: Joint Compression and Classification Network for Epilepsy Seizure Prediction](https://mlsanity.com/paper/2110.13674/) (2021)
- [Bridging the Gap Between Patient-specific and Patient-independent Seizure Prediction via Knowledge Distillation](https://mlsanity.com/paper/2202.12598/) (2022)
- [Deep Learning-Powered Electrical Brain Signals Analysis: Advancing Neurological Diagnostics](https://mlsanity.com/paper/2502.17213/) (2025)
- [Supervised and Unsupervised Deep Learning Approaches for EEG Seizure Prediction](https://mlsanity.com/paper/2304.14922/) (2023)
- [A Memristor-Inspired Computation for Epileptiform Signals in Spheroids](https://mlsanity.com/paper/2307.04607/) (2023)

## Abstract (arXiv)

An accurate seizure prediction system enables early warnings before seizure onset of epileptic patients. It is extremely important for drug-refractory patients. Conventional seizure prediction works usually rely on features extracted from Electroencephalography (EEG) recordings and classification algorithms such as regression or support vector machine (SVM) to locate the short time before seizure onset. However, such methods cannot achieve high-accuracy prediction due to information loss of the hand-crafted features and the limited classification ability of regression and SVM algorithms. We propose an end-to-end deep learning solution using a convolutional neural network (CNN) in this paper. One and two dimensional kernels are adopted in the early- and late-stage convolution and max-pooling layers, respectively. The proposed CNN model is evaluated on Kaggle intracranial and CHB-MIT scalp EEG datasets. Overall sensitivity, false prediction rate, and area under receiver operating characteristic curve reaches 93.5%, 0.063/h, 0.981 and 98.8%, 0.074/h, 0.988 on two datasets respectively. Comparison with state-of-the-art works indicates that the proposed model achieves exceeding prediction performance.