0000000000172381

AUTHOR

Aurora L. R. Martins

0000-0002-7833-9907

showing 2 related works from this author

Multivariate and Multiscale Complexity of Long-Range Correlated Cardiovascular and Respiratory Variability Series

2020

Assessing the dynamical complexity of biological time series represents an important topic with potential applications ranging from the characterization of physiological states and pathological conditions to the calculation of diagnostic parameters. In particular, cardiovascular time series exhibit a variability produced by different physiological control mechanisms coupled with each other, which take into account several variables and operate across multiple time scales that result in the coexistence of short term dynamics and long-range correlations. The most widely employed technique to evaluate the dynamical complexity of a time series at different time scales, the so-called multiscale …

Multivariate statisticsSystolic arterial pressure (SAP)Vector autoregressive fractionally integrated (VARFI) modelsComputer scienceGeneral Physics and Astronomylcsh:Astrophysics01 natural sciencesArticle010305 fluids & plasmaslcsh:QB460-4660103 physical sciencesRange (statistics)Multi-scale entropy (MSE)lcsh:Science010306 general physicsRepresentation (mathematics)Parametric statisticsvector autoregressive fractionally integrated (VARFI) modelSeries (mathematics)multi-scale entropy (MSE)Stochastic processsystolic arterial pressure (SAP)lcsh:QC1-999Term (time)Autoregressive modelSettore ING-INF/06 - Bioingegneria Elettronica E Informaticavector autoregressive fractionally integrated (VARFI) modelslcsh:QBiological systemHeart rate variability (HRV)lcsh:Physicsheart rate variability (HRV)
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Vector Autoregressive Fractionally Integrated Models to Assess Multiscale Complexity in Cardiovascular and Respiratory Time Series

2020

Cardiovascular variability is the result of the activity of several physiological control mechanisms, which involve different variables and operate across multiple time scales encompassing short term dynamics and long range correlations. This study presents a new approach to assess the multiscale complexity of multivariate time series, based on linear parametric models incorporating autoregressive coefficients and fractional integration. The approach extends to the multivariate case recent works introducing a linear parametric representation of multiscale entropy, and is exploited to assess the complexity of cardiovascular and respiratory time series in healthy subjects studied during postu…

Multivariate statisticsvector autoregressive fractionally integrated (VARFI) modelComputer scienceQuantitative Biology::Tissues and OrgansPhysics::Medical Physicssystolic arterial pressure (SAP)Cardiovascular variabilitycomputer.software_genreCorrelationAutoregressive modelmultiscale entropy (MSE)heart period (HP)Settore ING-INF/06 - Bioingegneria Elettronica E InformaticaParametric modelMultiple timeEntropy (information theory)Data miningTime seriescomputerParametric statistics2020 11th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO)
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