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Comparative study of entropy sensitivity to missing biosignal data

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Comparative study of entropy sensitivity to missing biosignal data

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dc.contributor.author Cirugeda Roldán, Eva María es_ES
dc.contributor.author Cuesta Frau, David es_ES
dc.contributor.author Miró Martínez, Pau es_ES
dc.contributor.author Oltra Crespo, Sandra es_ES
dc.date.accessioned 2015-06-22T11:58:34Z
dc.date.available 2015-06-22T11:58:34Z
dc.date.issued 2014-11
dc.identifier.issn 1099-4300
dc.identifier.uri http://hdl.handle.net/10251/52111
dc.description.abstract Entropy estimation metrics have become a widely used method to identify subtle changes or hidden features in biomedical records. These methods have been more effective than conventional linear techniques in a number of signal classification applications, specially the healthy pathological segmentation dichotomy. Nevertheless, a thorough characterization of these measures, namely, how to match metric and signal features, is still lacking. This paper studies a specific characterization problem: the influence of missing samples in biomedical records. The assessment is conducted using four of the most popular entropy metrics: Approximate Entropy, Sample Entropy, Fuzzy Entropy, and Detrended Fluctuation Analysis. The rationale of this study is that missing samples are a signal disturbance that can arise in many cases: signal compression, non-uniform sampling, or data transmission stages. It is of great interest to determine if these real situations can impair the capability of segmenting signal classes using such metrics. The experiments employed several biosignals: electroencephalograms, gait records, and RR time series. Samples of these signals were systematically removed, and the entropy computed for each case. The results showed that these metrics are robust against missing samples: With a data loss percentage of 50% or even higher, the methods were still able to distinguish among signal classes. es_ES
dc.description.sponsorship This work has been supported by the Spanish Ministry of Science and Innovation, research project TEC2009-14222. en_EN
dc.language Inglés es_ES
dc.publisher MDPI es_ES
dc.relation.ispartof Entropy es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Approximate entropy es_ES
dc.subject Sample entropy es_ES
dc.subject Fuzzy entropy es_ES
dc.subject Detrended fluctuation analysis es_ES
dc.subject Biosignal classification es_ES
dc.subject Data loss es_ES
dc.subject.classification ESTADISTICA E INVESTIGACION OPERATIVA es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.subject.classification MATEMATICA APLICADA es_ES
dc.title Comparative study of entropy sensitivity to missing biosignal data es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/e16115901
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//TEC2009-14222/ES/Interpretacion Y Caracterizacion De Metodos De Analisis De Complejidad En El Contexto Del Procesado Biomedico De La Señal/ / es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario Mixto Tecnológico de Informática - Institut Universitari Mixt Tecnològic d'Informàtica es_ES
dc.description.bibliographicCitation Cirugeda Roldan, EM.; Cuesta Frau, D.; Miró Martínez, P.; Oltra Crespo, S. (2014). Comparative study of entropy sensitivity to missing biosignal data. Entropy. 16(11):5901-5918. doi:10.3390/e16115901 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.3390/e16115901 es_ES
dc.description.upvformatpinicio 5901 es_ES
dc.description.upvformatpfin 5918 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 16 es_ES
dc.description.issue 11 es_ES
dc.relation.senia 277484
dc.contributor.funder Ministerio de Ciencia e Innovación es_ES
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