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Topolnogical classifier for detecting the emergence of epileptic seizures
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 t...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6003048/ https://www.ncbi.nlm.nih.gov/pubmed/29903043 http://dx.doi.org/10.1186/s13104-018-3482-7 |
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author | Piangerelli, Marco Rucco, Matteo Tesei, Luca Merelli, Emanuela |
author_facet | Piangerelli, Marco Rucco, Matteo Tesei, Luca Merelli, Emanuela |
author_sort | Piangerelli, Marco |
collection | PubMed |
description | 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 [Formula: see text] while the performance of a classifier based on Sample Entropy has an AUC equal to 62.0%. |
format | Online Article Text |
id | pubmed-6003048 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-60030482018-07-06 Topolnogical classifier for detecting the emergence of epileptic seizures Piangerelli, Marco Rucco, Matteo Tesei, Luca Merelli, Emanuela BMC Res Notes Research Note 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 [Formula: see text] while the performance of a classifier based on Sample Entropy has an AUC equal to 62.0%. BioMed Central 2018-06-14 /pmc/articles/PMC6003048/ /pubmed/29903043 http://dx.doi.org/10.1186/s13104-018-3482-7 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Note Piangerelli, Marco Rucco, Matteo Tesei, Luca Merelli, Emanuela Topolnogical classifier for detecting the emergence of epileptic seizures |
title | Topolnogical classifier for detecting the emergence of epileptic seizures |
title_full | Topolnogical classifier for detecting the emergence of epileptic seizures |
title_fullStr | Topolnogical classifier for detecting the emergence of epileptic seizures |
title_full_unstemmed | Topolnogical classifier for detecting the emergence of epileptic seizures |
title_short | Topolnogical classifier for detecting the emergence of epileptic seizures |
title_sort | topolnogical classifier for detecting the emergence of epileptic seizures |
topic | Research Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6003048/ https://www.ncbi.nlm.nih.gov/pubmed/29903043 http://dx.doi.org/10.1186/s13104-018-3482-7 |
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