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MuTE: a new matlab toolbox for estimating the multivariate transfer entropy in physiological variability series

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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.

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MLA
Montalto, Alessandro, et al. “MuTE: A New Matlab Toolbox for Estimating the Multivariate Transfer Entropy in Physiological Variability Series.” 2014 8TH CONFERENCE OF THE EUROPEAN STUDY GROUP ON CARDIOVASCULAR OSCILLATIONS (ESGCO), 2014, pp. 61–62.
APA
Montalto, A., Faes, L., & Marinazzo, D. (2014). MuTE: a new matlab toolbox for estimating the multivariate transfer entropy in physiological variability series. In 2014 8TH CONFERENCE OF THE EUROPEAN STUDY GROUP ON CARDIOVASCULAR OSCILLATIONS (ESGCO) (pp. 61–62). Trento, ITALY.
Chicago author-date
Montalto, Alessandro, Luca Faes, and Daniele Marinazzo. 2014. “MuTE: A New Matlab Toolbox for Estimating the Multivariate Transfer Entropy in Physiological Variability Series.” In 2014 8TH CONFERENCE OF THE EUROPEAN STUDY GROUP ON CARDIOVASCULAR OSCILLATIONS (ESGCO), 61–62.
Chicago author-date (all authors)
Montalto, Alessandro, Luca Faes, and Daniele Marinazzo. 2014. “MuTE: A New Matlab Toolbox for Estimating the Multivariate Transfer Entropy in Physiological Variability Series.” In 2014 8TH CONFERENCE OF THE EUROPEAN STUDY GROUP ON CARDIOVASCULAR OSCILLATIONS (ESGCO), 61–62.
Vancouver
1.
Montalto A, Faes L, Marinazzo D. MuTE: a new matlab toolbox for estimating the multivariate transfer entropy in physiological variability series. In: 2014 8TH CONFERENCE OF THE EUROPEAN STUDY GROUP ON CARDIOVASCULAR OSCILLATIONS (ESGCO). 2014. p. 61–2.
IEEE
[1]
A. Montalto, L. Faes, and D. Marinazzo, “MuTE: a new matlab toolbox for estimating the multivariate transfer entropy in physiological variability series,” in 2014 8TH CONFERENCE OF THE EUROPEAN STUDY GROUP ON CARDIOVASCULAR OSCILLATIONS (ESGCO), Trento, ITALY, 2014, pp. 61–62.
@inproceedings{8026197,
  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.},
  author       = {Montalto, Alessandro and Faes, Luca and Marinazzo, Daniele},
  booktitle    = {2014 8TH CONFERENCE OF THE EUROPEAN STUDY GROUP ON CARDIOVASCULAR OSCILLATIONS (ESGCO)},
  isbn         = {978-1-4799-3969-5},
  language     = {eng},
  location     = {Trento, ITALY},
  pages        = {61--62},
  title        = {MuTE: a new matlab toolbox for estimating the multivariate transfer entropy in physiological variability series},
  year         = {2014},
}

Web of Science
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