# Random Forest classifier for EEG-based seizure prediction

arXiv: https://arxiv.org/abs/2106.04510 · HTML: https://mlsanity.com/paper/2106.04510/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 38 references that are in the map.
- First posted on arXiv 2 Jun 2021.

## Builds on (most cited first, 2 of 2)

- [Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review](https://mlsanity.com/paper/2002.01925/) (2020)
- [Focal onset seizure prediction using convolutional networks](https://mlsanity.com/paper/1805.11576/) (2018)

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

- [EEGAgent: A Unified Framework for Automated EEG Analysis Using Large Language Models](https://mlsanity.com/paper/2511.09947/) (2025)
- [Hybrid Ensemble Learning for EEG-Based Epileptic Seizure Forecasting](https://mlsanity.com/paper/2609.35876/) (2026)

## Abstract (arXiv)

Epileptic seizure prediction has gained considerable interest in the computational Epilepsy research community. This paper presents a Machine Learning based method for epileptic seizure prediction which outperforms state-of-the art methods. We compute a probability for a given epoch, of being pre-ictal against interictal using the Random Forest classifier and introduce new concepts to enhance the robustness of the algorithm to false alarms. We assessed our method on 20 patients of the benchmark scalp EEG CHB-MIT dataset for a seizure prediction horizon (SPH) of 5 minutes and a seizure occurrence period (SOP) of 30 minutes. Our approach achieves a sensitivity of 82.07 % and a low false positive rate (FPR) of 0.0799 /h. We also tested our approach on intracranial EEG recordings.