# Supervised and Unsupervised Deep Learning Approaches for EEG Seizure Prediction

arXiv: https://arxiv.org/abs/2304.14922 · HTML: https://mlsanity.com/paper/2304.14922/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 54 references that are in the map.
- First posted on arXiv 24 Apr 2023.

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

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network](https://mlsanity.com/paper/1808.03314/) (2018)
- [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)
- [An End-to-End Deep Learning Approach for Epileptic Seizure Prediction](https://mlsanity.com/paper/2108.07453/) (2021)
- [Detecting Disengagement in Virtual Learning as an Anomaly using Temporal Convolutional Network Autoencoder](https://mlsanity.com/paper/2211.06870/) (2022)
- [Spatio-Temporal Adversarial Learning for Detecting Unseen Falls](https://mlsanity.com/paper/1905.07817/) (2019)
- [Detecting Falls with X-Factor Hidden Markov Models](https://mlsanity.com/paper/1504.02141/) (2015)

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

- [SincVAE: A new semi-supervised approach to improve anomaly detection on EEG data using SincNet and variational autoencoder](https://mlsanity.com/paper/2406.17537/) (2024)
- [DeepTokenEEG Enhancing Mild Cognitive Impairment and Alzheimers Classification via Tokenized EEG Features](https://mlsanity.com/paper/2605.15009/) (2026)

## Abstract (arXiv)

Epilepsy affects more than 50 million people worldwide, making it one of the world's most prevalent neurological diseases. The main symptom of epilepsy is seizures, which occur abruptly and can cause serious injury or death. The ability to predict the occurrence of an epileptic seizure could alleviate many risks and stresses people with epilepsy face. We formulate the problem of detecting preictal (or pre-seizure) with reference to normal EEG as a precursor to incoming seizure. To this end, we developed several supervised deep learning approaches to identify preictal EEG from normal EEG. We further develop novel unsupervised deep learning approaches to train the models on only normal EEG, and detecting pre-seizure EEG as an anomalous event. These deep learning models were trained and evaluated on two large EEG seizure datasets in a person-specific manner. We found that both supervised and unsupervised approaches are feasible; however, their performance varies depending on the patient, approach and architecture. This new line of research has the potential to develop therapeutic interventions and save human lives.