Objective An innovative method based on topological data analysis is introduced for classifying EEG recordings of patients affected by epilepsy. We construct a topological space from a collection of EEGs signals using Persistent Homology; then, we analyse the space by Persistent entropy, a global topological feature, in order to classify healthy and epileptic signals. Results The performance of the resulting one-feature-based linear topological classifier is tested by analysing the Physionet dataset. The quality of classification is evaluated in terms of the Area Under Curve (AUC) of the receiver operating characteristic curve. It is shown that the linear topological classifier has an AUC equal to 97.2% while the performance of a classifier based on Sample Entropy has an AUC equal to 62.0%.

Topological classifier for detecting the emergence of epileptic seizures

Marco Piangerelli;Matteo Rucco;Luca Tesei;Emanuela Merelli
2018-01-01

Abstract

Objective An innovative method based on topological data analysis is introduced for classifying EEG recordings of patients affected by epilepsy. We construct a topological space from a collection of EEGs signals using Persistent Homology; then, we analyse the space by Persistent entropy, a global topological feature, in order to classify healthy and epileptic signals. Results The performance of the resulting one-feature-based linear topological classifier is tested by analysing the Physionet dataset. The quality of classification is evaluated in terms of the Area Under Curve (AUC) of the receiver operating characteristic curve. It is shown that the linear topological classifier has an AUC equal to 97.2% while the performance of a classifier based on Sample Entropy has an AUC equal to 62.0%.
2018
262
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11581/408184
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