MuTE: A new Matlab toolbox for estimating the multivariate Transfer entropy in physiological variability series

Luca Faes, Daniele Marinazzo, Luca Faes, Alessandro Montalto

Risultato della ricerca: Other

1 Citazione (Scopus)

Abstract

We present a new time series analysis toolbox, developed in Matlab, for the estimation of the Transfer entropy (TE) between time series taken from a multivariate dataset. The main feature of the toolbox is its fully multivariate implementation, that is made possible by the design of an approach for the non-uniform embedding (NUE) of the observed time series. The toolbox is equipped with parametric (linear) and non-parametric (based on binning or nearest neighbors) entropy estimators. All these estimators, implemented using the NUE approach in comparison with the classical approach based on uniform embedding, are tested on RR interval, systolic pressure and respiration variability series measured from healthy subjects during head-up tilt. The results support the necessity of resorting to NUE for obtaining reliable estimates of the multivariate TE in short-term cardiovascular and cardiorespiratory variability. © 2014 IEEE.
Lingua originaleEnglish
Pagine59-60
Numero di pagine2
Stato di pubblicazionePublished - 2014

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Entropy
Time series
Time series analysis

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering

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MuTE: A new Matlab toolbox for estimating the multivariate Transfer entropy in physiological variability series. / Faes, Luca; Marinazzo, Daniele; Faes, Luca; Montalto, Alessandro.

2014. 59-60.

Risultato della ricerca: Other

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AB - We present a new time series analysis toolbox, developed in Matlab, for the estimation of the Transfer entropy (TE) between time series taken from a multivariate dataset. The main feature of the toolbox is its fully multivariate implementation, that is made possible by the design of an approach for the non-uniform embedding (NUE) of the observed time series. The toolbox is equipped with parametric (linear) and non-parametric (based on binning or nearest neighbors) entropy estimators. All these estimators, implemented using the NUE approach in comparison with the classical approach based on uniform embedding, are tested on RR interval, systolic pressure and respiration variability series measured from healthy subjects during head-up tilt. The results support the necessity of resorting to NUE for obtaining reliable estimates of the multivariate TE in short-term cardiovascular and cardiorespiratory variability. © 2014 IEEE.

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