Topological classifier for detecting the emergence of epileptic seizures

Abstract

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.

Open data: Physionet data-set

References in corpus (2)

• Topological data analysis of contagion maps for examining spreading processes on networks
• A Fast, robust algorithm for power line interference cancellation in neural recording

Cited by in corpus (7)

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• Topological EEG Nonlinear Dynamics Analysis for Emotion Recognition
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• Towards a Quantitative Theory of Digraph-Based Complexes and its Applications in Brain Network Analysis