Selection of Entropy Based Features for Automatic Analysis of Essential Tremor
dc.contributor.author | López de Ipiña Peña, Miren Karmele | |
dc.contributor.author | Sole-Casals, Jordi | |
dc.contributor.author | Faúndez Zanuy, Marcos | |
dc.contributor.author | Calvo Salomón, Pilar María | |
dc.contributor.author | Sesa, Enric | |
dc.contributor.author | Martinez de Lizarduy Sturtze, Unai | |
dc.contributor.author | De la Riva, Patricia | |
dc.contributor.author | Martí Massó, José Félix | |
dc.contributor.author | Beitia Bengoa, Blanca | |
dc.contributor.author | Bergareche, Alberto | |
dc.date.accessioned | 2018-04-23T16:03:02Z | |
dc.date.available | 2018-04-23T16:03:02Z | |
dc.date.issued | 2016-05 | |
dc.identifier.citation | Entropy 18(5) : (2016) // Article ID 184 | es_ES |
dc.identifier.issn | 1099-4300 | |
dc.identifier.uri | http://hdl.handle.net/10810/26558 | |
dc.description | This paper is an extended version of one paper published in the 4th IEEE International Work Conference on Bioinspired Intelligence, Donostia, Spain, 9–12 June 2015 | es_ES |
dc.description.abstract | Biomedical systems produce biosignals that arise from interaction mechanisms. In a general form, those mechanisms occur across multiple scales, both spatial and temporal, and contain linear and non-linear information. In this framework, entropy measures are good candidates in order provide useful evidence about disorder in the system, lack of information in time-series and/or irregularity of the signals. The most common movement disorder is essential tremor (ET), which occurs 20 times more than Parkinson's disease. Interestingly, about 50%-70% of the cases of ET have a genetic origin. One of the most used standard tests for clinical diagnosis of ET is Archimedes' spiral drawing. This work focuses on the selection of non-linear biomarkers from such drawings and handwriting, and it is part of a wider cross study on the diagnosis of essential tremor, where our piece of research presents the selection of entropy features for early ET diagnosis. Classic entropy features are compared with features based on permutation entropy. Automatic analysis system settled on several Machine Learning paradigms is performed, while automatic features selection is implemented by means of ANOVA (analysis of variance) test. The obtained results for early detection are promising and appear applicable to real environments. | es_ES |
dc.description.sponsorship | This work has been partially supported by the University of the Basque Country under project ref. UPV/EHU-58/14, SAIOTEK program and others from the Basque Government, the Spanish Ministerio de Ciencia e Innovacion TEC2012-38630-C04-03, the University of Vic-Central University of Catalonia under the research grant R0904, INNPACTO program from the Spanish Government, and UPV/EHU Summer Courses Foundation. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.relation | info:eu-repo/grantAgreement/MICIIN/TEC2012-38630-C04-03 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | permutation entropy | es_ES |
dc.subject | essential tremor | es_ES |
dc.subject | automatic drawing analysis | es_ES |
dc.subject | Archimedes' spiral | es_ES |
dc.subject | non-linear features | es_ES |
dc.subject | automatic feature selection | es_ES |
dc.title | Selection of Entropy Based Features for Automatic Analysis of Essential Tremor | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/). | es_ES |
dc.rights.holder | Atribución 3.0 España | * |
dc.relation.publisherversion | http://www.mdpi.com/1099-4300/18/5/184 | es_ES |
dc.identifier.doi | 10.3390/e18050184 | |
dc.departamentoes | Ingeniería de sistemas y automática | es_ES |
dc.departamentoeu | Sistemen ingeniaritza eta automatika | es_ES |
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Except where otherwise noted, this item's license is described as © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).