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.

Martins, A., Amado, C., Rocha, A.P., Silva, M.E., Pernice, R., Javorka, M., et al. (2020). Vector Autoregressive Fractionally Integrated Models to Assess Multiscale Complexity in Cardiovascular and Respiratory Time Series. In 2020 11th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO) (pp. 1-2) [10.1109/ESGCO49734.2020.9158136].

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

Pernice, Riccardo;Faes, Luca
2020

Abstract

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.
Settore ING-INF/06 - Bioingegneria Elettronica E Informatica
978-1-7281-5751-1
Martins, A., Amado, C., Rocha, A.P., Silva, M.E., Pernice, R., Javorka, M., et al. (2020). Vector Autoregressive Fractionally Integrated Models to Assess Multiscale Complexity in Cardiovascular and Respiratory Time Series. In 2020 11th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO) (pp. 1-2) [10.1109/ESGCO49734.2020.9158136].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10447/430394
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