# Automatic Seizure Prediction using CNN and LSTM

arXiv: https://arxiv.org/abs/2211.02679 · HTML: https://mlsanity.com/paper/2211.02679/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 41 references that are in the map.
- First posted on arXiv 28 Oct 2022.

## Builds on (most cited first, 1 of 1)

- [Focal onset seizure prediction using convolutional networks](https://mlsanity.com/paper/1805.11576/) (2018)

## Built on by (most cited first, 1 of 1)

- [Exploring Spiking Neural Networks for Binary Classification in Multivariate Time Series at the Edge](https://mlsanity.com/paper/2510.20997/) (2025)

## Abstract (arXiv)

The electroencephalogram (EEG) is one of the most precious technologies to understand the happenings inside our brain and further understand our body's happenings. Automatic prediction of oncoming seizures using the EEG signals helps the doctors and clinical experts and reduces their workload. This paper proposes an end-to-end deep learning algorithm to fully automate seizure prediction's laborious task without any heavy pre-processing on the EEG data or feature engineering. The proposed deep learning network is a blend of signal processing and deep learning pipeline, which automates the seizure prediction framework using the EEG signals. This proposed model was evaluated on an open EEG dataset, CHB-MIT. The network achieved an average sensitivity of 97.746% and a false positive rate (FPR) of 0.2373 per hour.