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Depositordc.contributorAzami, Hamed
Funderdc.contributor.otherUniversity of Edinburghen_UK
Data Creatordc.creatorAzami, Hamed
Data Creatordc.creatorEscudero, Javier
Date Accessioneddc.date.accessioned2016-09-07T16:33:39Z
Date Availabledc.date.available2016-10-01T04:15:27Z
Citationdc.identifier.citationAzami, Hamed; Escudero, Javier. (2016). Matlab codes for "Refined Multiscale Fuzzy Entropy based on Standard Deviation for Biomedical Signal Analysis", [software]. University of Edinburgh, School of Engineering, Institute for Digital Communications. https://doi.org/10.7488/ds/1477.en
Persistent Identifierdc.identifier.urihttp://hdl.handle.net/10283/2099
Persistent Identifierdc.identifier.urihttps://doi.org/10.7488/ds/1477
Dataset Description (abstract)dc.description.abstractMultiscale entropy (MSE) has been a prevalent algorithm to quantify the complexity of fluctuations in the local mean value of biomedical time series. Recent developments in the field have tried to improve the MSE by reducing its variability in large scale factors. On the other hand, there has been recent interest in using other statistical moments than the mean, i.e. variance, in the coarse-graining step of the MSE. Building on these trends, here we introduce the so-called refined composite multiscale fuzzy entropy based on the standard deviation (RCMFEσ) to quantify the dynamical properties of spread over multiple time scales. We demonstrate the dependency of the RCMFEσ, in comparison with other multiscale approaches, on several straightforward signal processing concepts using a set of synthetic signals. We also investigate the complementarity of using the standard deviation instead of the mean in the coarse-graining process using magnetoencephalograms in Alzheimer’s disease and publicly available electroencephalograms recorded from focal and non-focal areas in epilepsy. Our results indicate that RCMFEσ offers complementary information to that revealed by classical coarse-graining approaches and that it has superior performance to distinguish different types of physiological activity. The codes for our analysis, including sample entropy, fuzzy entropy, MSE based on mean (MSEμ), MFEμ, RCMSEμ, RCMFEμ, MSE based on variance (MSEσ2) , MFEσ2 , RCMSEσ2 , RCMFEσ2, MSEσ, MFEσ, RCMSEσ, and RCMFEσ are available here.en_UK
Languagedc.language.isoengen_UK
Publisherdc.publisherUniversity of Edinburgh. School of Engineering. Institute for Digital Communicationsen_UK
Relation (Is Referenced By)dc.relation.isreferencedbyH. Azami and J. Escudero, "Refined Multiscale Fuzzy Entropy based on Standard Deviation for Biomedical Signal Analysis", Medical & Biological Engineering & Computing, 2016.
Rightsdc.rightsCreative Commons Attribution 4.0 International Public Licenseen
Subjectdc.subjectgeneralized multiscale entropy
Subjectdc.subjectstatistical moments
Subjectdc.subjectfuzzy entropy
Subjectdc.subjectsample entropy
Subjectdc.subjectbiomedical signals
Subject Classificationdc.subject.classificationEngineering::Bioengineeringen_UK
Titledc.titleMatlab codes for "Refined Multiscale Fuzzy Entropy based on Standard Deviation for Biomedical Signal Analysis"en_UK
Typedc.typesoftwareen_UK

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