Multiscale partial information decomposition of dynamic processes with short and long-range correlations: theory and application to cardiovascular control

• Type: Article in International Scientific Journal
• Date: 2022

Authors

Pinto, H (Author) Other; Pernice, R (Author) Other; Maria Eduarda Silva (Author) FEP; Javorka, M (Author) Other; Faes, L (Author) Other; Rocha, AP (Author) FCUP

Journal

Title: Physiological Measurement

Vol. 43 No. 8

ISSN: 0967-3334

Publisher: Institute of Physics Publishing

Indexing

ISI Web of Knowledge - 6 Citations

Scopus - 9 Citations

Other information

Authenticus ID: P-00W-WRB

DOI: 10.1088/1361-6579/ac826c

Abstract (EN): Objective. In this work, an analytical framework for the multiscale analysis of multivariate Gaussian processes is presented, whereby the computation of Partial Information Decomposition measures is achieved accounting for the simultaneous presence of short-term dynamics and long-range correlations. Approach. We consider physiological time series mapping the activity of the cardiac, vascular and respiratory systems in the field of Network Physiology. In this context, the multiscale representation of transfer entropy within the network of interactions among Systolic arterial pressure (S), respiration (R) and heart period (H), as well as the decomposition into unique, redundant and synergistic contributions, is obtained using a Vector AutoRegressive Fractionally Integrated (VARFI) framework for Gaussian processes. This novel approach allows to quantify the directed information flow accounting for the simultaneous presence of short-term dynamics and long-range correlations among the analyzed processes. Additionally, it provides analytical expressions for the computation of the information measures, by exploiting the theory of state space models. The approach is first illustrated in simulated VARFI processes and then applied to H, S and R time series measured in healthy subjects monitored at rest and during mental and postural stress. Main Results. We demonstrate the ability of the VARFI modeling approach to account for the coexistence of short-term and long-range correlations in the study of multivariate processes. Physiologically, we show that postural stress induces larger redundant and synergistic effects from S and R to H at short time scales, while mental stress induces larger information transfer from S to H at longer time scales, thus evidencing the different nature of the two stressors. Significance. The proposed methodology allows to extract useful information about the dependence of the information transfer on the balance between short-term and long-range correlations in coupled dynamical systems, which cannot be observed using standard methods that do not consider long-range correlations.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 19