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Selection of Entropy Based Features for Automatic Analysis of Essential Tremor

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dc.contributor Universitat de Vic - Universitat Central de Catalunya. Grup de Recerca en Tractament de Dades i senyals
dc.contributor.author Lopez-de-Ipiña, Karmele
dc.contributor.author Solé-Casals, Jordi
dc.contributor.author Faundez-Zanuy, Marcos
dc.contributor.author Calvo, Pilar
dc.contributor.author Sesa Nogueras, Enric
dc.contributor.author Martinez de Lizardui, Unai
dc.contributor.author De La Riva, Patricia
dc.contributor.author Marti-Masso, Jose F.
dc.contributor.author Beitia, Blanca
dc.contributor.author Bergareche, Alberto
dc.date.accessioned 2017-05-23T18:53:16Z
dc.date.available 2017-05-23T18:53:16Z
dc.date.created 2016
dc.date.issued 2016
dc.identifier.issn 1099-4300
dc.identifier.uri http://hdl.handle.net/10854/5001
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
dc.format application/pdf
dc.format.extent 22 p. es
dc.language.iso eng es
dc.publisher MDPI es
dc.rights Aquest document està subjecte a aquesta llicència Creative Commons es
dc.rights.uri http://creativecommons.org/licenses/by/4.0/ es
dc.subject.other Alzheimer, Malaltia d' es
dc.title Selection of Entropy Based Features for Automatic Analysis of Essential Tremor es
dc.type info:eu-repo/semantics/article es
dc.identifier.doi https://doi.org/10.3390/e18050184
dc.rights.accesRights info:eu-repo/semantics/openAccess es
dc.type.version info:eu-repo/publishedVersion es
dc.indexacio Indexat a WOS/JCR es

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