  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.
