# Topological classifier for detecting the emergence of epileptic seizures

arXiv: https://arxiv.org/abs/1611.04872 · HTML: https://mlsanity.com/paper/1611.04872/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2016.

## Built on by (most cited first, 3 of 3)

- [Topological EEG Nonlinear Dynamics Analysis for Emotion Recognition](https://mlsanity.com/paper/2203.06895/) (2022)
- [Persistent reachability homology in machine learning applications](https://mlsanity.com/paper/2511.04825/) (2025)
- [Persistent Entropy as a Detector of Phase Transitions](https://mlsanity.com/paper/2602.09058/) (2026)

## Abstract (arXiv)

In this work we study how to apply topological data analysis to create a method suitable to classify EEGs of patients affected by epilepsy. The topological space constructed from the collection of EEGs signals is analyzed by Persistent Entropy acting as a global topological feature for discriminating between healthy and epileptic signals. The Physionet data-set has been used for testing the classifier.