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RESEARCH PRODUCT

Vector Autoregressive Fractionally Integrated Models to Assess Multiscale Complexity in Cardiovascular and Respiratory Time Series

Michal JavorkaLuca FaesAna Paula RochaAurora L. R. MartinsRiccardo PerniceCelestino AmadoMaria Eduarda Silva

subject

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 statistics

description

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 postural and mental stress. published

yearjournalcountryeditionlanguage
2020-07-152020 11th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO)
https://doi.org/10.1109/esgco49734.2020.9158136