Heart Period (H) results from the activity of several coexisting control mechanisms, involving Systolic Arterial Pressure (S) and Respiration (R), which operate across multiple time scales encompassing not only short-term dynamics but also long-range correlations. In this work, multiscale representation of Transfer Entropy (TE) and of its decomposition in the network of these three interacting processes is obtained by extending the multivariate approach based on linear parametric VAR models to the Vector AutoRegressive Fractionally Integrated (VARFI) framework for Gaussian processes. This approach allows to dissect the different contributions to cardiac dynamics accounting for the simultaneous presence of short and long term dynamics. The proposed method is first tested on simulations of a benchmark VARFI model and then applied to experimental data consisting of H, S and R time series measured in healthy subjects monitored at rest and during mental and postural stress. The results reveal that the proposed method can highlight the dependence of the information transfer on the balance between short-term and long-range correlations in coupled dynamical systems.

Pinto, H., Pernice, R., Amado, C., Silva, M.E., Javorka, M., Faes, L., et al. (2021). Assessing Transfer Entropy in cardiovascular and respiratory time series under long-range correlations. In Proceedings 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 748-751) [10.1109/EMBC46164.2021.9630004].

Assessing Transfer Entropy in cardiovascular and respiratory time series under long-range correlations

Pernice, Riccardo;Faes, Luca;
2021-11

Abstract

Heart Period (H) results from the activity of several coexisting control mechanisms, involving Systolic Arterial Pressure (S) and Respiration (R), which operate across multiple time scales encompassing not only short-term dynamics but also long-range correlations. In this work, multiscale representation of Transfer Entropy (TE) and of its decomposition in the network of these three interacting processes is obtained by extending the multivariate approach based on linear parametric VAR models to the Vector AutoRegressive Fractionally Integrated (VARFI) framework for Gaussian processes. This approach allows to dissect the different contributions to cardiac dynamics accounting for the simultaneous presence of short and long term dynamics. The proposed method is first tested on simulations of a benchmark VARFI model and then applied to experimental data consisting of H, S and R time series measured in healthy subjects monitored at rest and during mental and postural stress. The results reveal that the proposed method can highlight the dependence of the information transfer on the balance between short-term and long-range correlations in coupled dynamical systems.
Settore ING-INF/06 - Bioingegneria Elettronica E Informatica
978-1-7281-1179-7
Pinto, H., Pernice, R., Amado, C., Silva, M.E., Javorka, M., Faes, L., et al. (2021). Assessing Transfer Entropy in cardiovascular and respiratory time series under long-range correlations. In Proceedings 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 748-751) [10.1109/EMBC46164.2021.9630004].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10447/526746
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