1,721,011 research outputs found
Matlab codes for "Refined Composite Multivariate Generalized Multiscale Fuzzy Entropy: A Tool for Complexity Analysis of Multichannel Signals"
No full textMultiscale entropy (MSE) is an appealing tool to characterize the complexity of time series over multiple temporal scales. Recent developments in the field have tried to extend the MSE technique in different ways. Building on these trends, we propose the so-called refined composite multivariate multiscale fuzzy entropy (RCmvMFE) whose coarse-graining step uses variance (RCmvMFEσ2) or mean (RCmvMFEµ). We investigate the behaviour of these multivariate methods on multichannel white Gaussian and 1/f noise signals, and two publicly available biomedical recordings. Our simulations demonstrate that RCmvMFEσ2 and RCmvMFEµ lead to more stable results and are less sensitive to the signals’ length in comparison with the other existing multivariate multiscale entropy-based methods. The classification results also show that using both the variance and mean in the coarse-graining step offer complexity profiles with complementary information for biomedical signal analysis. We made freely available all the Matlab codes used in this study, including mvSE, mvFE, mvMSEµ, RCmvMSEµ, mvMFEµ, RCmvMFEµ, mvMSEσ2, RCmvMSEσ2, mvMFEσ2 and RCmvMFEσ2.mvSE: multivariate sample entropy
mvFE: multivariate fuzzy entropy
mvMSE_mu: multivariate multiscale sample entropy whose coarse-graining is based on mean
RCmvMSE_mu: refined composite multivariate multiscale sample entropy whose coarse-graining is based on mean
mvMFE_mu: multivariate multiscale fuzzy entropy whose coarse-graining is based on mean
RCmvMFE_mu: refined composite multivariate multiscale fuzzy entropy whose coarse-graining is based on mean
mvMSE_var: multivariate multiscale fuzzy entropy whose coarse-graining is based on variance
RCmvMSE_var: refined composite multivariate multiscale sample entropy whose coarse-graining is based on variance
mvMFE_var: multivariate multiscale fuzzy entropy whose coarse-graining is based on variance
RCmvMFE_var: refined composite multivariate multiscale fuzzy entropy whose coarse-graining is based on variance
embd: multivariate delay embedded vector
SUPERSEDED - Matlab codes for "Refined Multiscale Fuzzy Entropy based on Standard Deviation for Biomedical Signal Analysis"
No full text## This item has been superseded. It contained code for Azami, H., Fernández, A. & Escudero, J. Refined multiscale fuzzy entropy based on standard deviation for biomedical signal analysis. Med Biol Eng Comput 55, 2037–2052 (2017). For up-to-date versions of this and other entropy analysis algorithms, consider visiting the GitHub repositories linked in the full item record. ##
Multiscale 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
Matlab codes for "Improved Multiscale Permutation Entropy for Biomedical Signal Analysis: Interpretation and Application to Electroencephalogram Recordings"
No full textPermutation entropy (PE) is a well-known and fast method extensively used in many physiological signal processing applications to measure the irregularity of time series. Multiscale PE (MPE) is based on assessing the PE for a number of coarse-grained sequences representing temporal scales. However, the stability of the conventional MPE may be compromised for short time series. Here, we propose an improved MPE (IMPE) to reduce the variability of entropy measures over long temporal scales, leading to more reliable and stable results. We gain insight into the dependency of MPE and IMPE on several straightforward signal processing concepts which appear in biomedical activity via a set of synthetic signals. We also apply these techniques to real biomedical signals via publicly available electroencephalogram (EEG) recordings acquired with eyes open and closed and to ictal and non-ictal intracranial EEGs. We conclude that IMPE improves the reliability of the entropy estimations in comparison with the traditional MPE and that it is a promising technique to characterize physiological changes affecting several temporal scales. We provide the codes of the synthetic signals and IMPE in the public domain
Matlab codes for "Refined Composite Multivariate Generalized Multiscale Fuzzy Entropy: A Tool for Complexity Analysis of Multichannel Signals"
No full textMultiscale entropy (MSE) is an appealing tool to characterize the complexity of time series over multiple temporal scales. Recent developments in the field have tried to extend the MSE technique in different ways. Building on these trends, we propose the so-called refined composite multivariate multiscale fuzzy entropy (RCmvMFE) whose coarse-graining step uses variance (RCmvMFEσ2) or mean (RCmvMFEµ). We investigate the behaviour of these multivariate methods on multichannel white Gaussian and 1/f noise signals, and two publicly available biomedical recordings. Our simulations demonstrate that RCmvMFEσ2 and RCmvMFEµ lead to more stable results and are less sensitive to the signals’ length in comparison with the other existing multivariate multiscale entropy-based methods. The classification results also show that using both the variance and mean in the coarse-graining step offer complexity profiles with complementary information for biomedical signal analysis. We made freely available all the Matlab codes used in this study, including mvSE, mvFE, mvMSEµ, RCmvMSEµ, mvMFEµ, RCmvMFEµ, mvMSEσ2, RCmvMSEσ2, mvMFEσ2 and RCmvMFEσ2.Azami, Hamed; Escudero, Javier. (2016). Matlab codes for "Refined Composite Multivariate Generalized Multiscale Fuzzy Entropy: A Tool for Complexity Analysis of Multichannel Signals", [software]. University of Edinburgh, School of Engineering, Institute for Digital Communications. http://dx.doi.org/10.7488/ds/143
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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