# Convolutional Neural Networks for Epileptic Seizure Prediction

arXiv: https://arxiv.org/abs/1811.00915 · HTML: https://mlsanity.com/paper/1811.00915/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2018.

## Built on by (most cited first, 8 of 8)

- [An End-to-End Deep Learning Approach for Epileptic Seizure Prediction](https://mlsanity.com/paper/2108.07453/) (2021)
- [A New Neuromorphic Computing Approach for Epileptic Seizure Prediction](https://mlsanity.com/paper/2102.12773/) (2021)
- [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)
- [Investigating Brain Connectivity with Graph Neural Networks and GNNExplainer](https://mlsanity.com/paper/2206.01930/) (2022)
- [Supervised and Unsupervised Deep Learning Approaches for EEG Seizure Prediction](https://mlsanity.com/paper/2304.14922/) (2023)
- [Coherent False Seizure Prediction in Epilepsy, Coincidence or Providence?](https://mlsanity.com/paper/2110.13550/) (2021)
- [Path Signatures for Seizure Forecasting](https://mlsanity.com/paper/2308.09312/) (2023)

## Abstract (arXiv)

Epilepsy is the most common neurological disorder and an accurate forecast of seizures would help to overcome the patient's uncertainty and helplessness. In this contribution, we present and discuss a novel methodology for the classification of intracranial electroencephalography (iEEG) for seizure prediction. Contrary to previous approaches, we categorically refrain from an extraction of hand-crafted features and use a convolutional neural network (CNN) topology instead for both the determination of suitable signal characteristics and the binary classification of preictal and interictal segments. Three different models have been evaluated on public datasets with long-term recordings from four dogs and three patients. Overall, our findings demonstrate the general applicability. In this work we discuss the strengths and limitations of our methodology.