TY  - JOUR
AU  - Jovanović, Slađana
AU  - Skorić, Tamara
AU  - Sarenac, Olivera
AU  - Milutinović-Smiljanić, Sanja
AU  - Japundžić-Žigon, Nina
AU  - Bajić, Dragana
PY  - 2018
UR  - https://smile.stomf.bg.ac.rs/handle/123456789/2291
AB  - Objectives: Copula is a tool for measuring linear and non-linear interactions between two or more time series. The aim of this paper is to prove that a copula approach can accurately capture and visualize the spatial and temporal fluctuations in dependency structures of cardiovascular signals, and to outline the application possibilities. Methods: The method for measuring the level of interaction between systolic blood pressure and the corresponding pulse interval is validated statistically and pharmacologically. The time series are recorded from the freely moving male Wistar rats equipped with radio-telemetry device for blood pressure recording, before and after administration of autonomic blockers scopolamine, atenolol, prazosin and hexamethonium. Implicit (Gaussian and t) and explicit (Clayton, Frank and Gumbel) copulas were calculated and compared to the conventional bivariate methods (Kendal, Pearson, Spearman and classical correlation). Further statistical validation was done using artificially generated surrogate data. A window sliding procedure for dynamic monitoring the signals' coupling strength is implemented. Results: Under the baseline physiological conditions, SBP-PI dependency is significant for time lags 0 s-4 s. Hexamethonium completely abolished the dependency, scopolamine abolished it for time lags 0 s-2 s, atenolol first slightly increased, than for lags greater than 2 s decreased the dependency and prazosin had no effect. Isospectral and isodistributional surrogate data tests confirm that copulas successfully notify the absence of dependency as well. Conclusion: Copula approach accurately captures the temporal fluctuations in dependency structures of SBP and PI, simultaneously enabling a visualization of dependency levels within the particular signal zones. An analysis showed that copulas are more sensitive than the conventional statistical measures, with Frank copula exhibiting the best characterization of SBP and PI dependency.
PB  - Elsevier Sci Ltd, Oxford
T2  - Biomedical Signal Processing & Control
T1  - Copula as a dynamic measure of cardiovascular signal interactions
VL  - 43
SP  - 250
EP  - 264
DO  - 10.1016/j.bspc.2018.03.007
ER  - 

@article{
author = "Jovanović, Slađana and Skorić, Tamara and Sarenac, Olivera and Milutinović-Smiljanić, Sanja and Japundžić-Žigon, Nina and Bajić, Dragana",
year = "2018",
abstract = "Objectives: Copula is a tool for measuring linear and non-linear interactions between two or more time series. The aim of this paper is to prove that a copula approach can accurately capture and visualize the spatial and temporal fluctuations in dependency structures of cardiovascular signals, and to outline the application possibilities. Methods: The method for measuring the level of interaction between systolic blood pressure and the corresponding pulse interval is validated statistically and pharmacologically. The time series are recorded from the freely moving male Wistar rats equipped with radio-telemetry device for blood pressure recording, before and after administration of autonomic blockers scopolamine, atenolol, prazosin and hexamethonium. Implicit (Gaussian and t) and explicit (Clayton, Frank and Gumbel) copulas were calculated and compared to the conventional bivariate methods (Kendal, Pearson, Spearman and classical correlation). Further statistical validation was done using artificially generated surrogate data. A window sliding procedure for dynamic monitoring the signals' coupling strength is implemented. Results: Under the baseline physiological conditions, SBP-PI dependency is significant for time lags 0 s-4 s. Hexamethonium completely abolished the dependency, scopolamine abolished it for time lags 0 s-2 s, atenolol first slightly increased, than for lags greater than 2 s decreased the dependency and prazosin had no effect. Isospectral and isodistributional surrogate data tests confirm that copulas successfully notify the absence of dependency as well. Conclusion: Copula approach accurately captures the temporal fluctuations in dependency structures of SBP and PI, simultaneously enabling a visualization of dependency levels within the particular signal zones. An analysis showed that copulas are more sensitive than the conventional statistical measures, with Frank copula exhibiting the best characterization of SBP and PI dependency.",
publisher = "Elsevier Sci Ltd, Oxford",
journal = "Biomedical Signal Processing & Control",
title = "Copula as a dynamic measure of cardiovascular signal interactions",
volume = "43",
pages = "250-264",
doi = "10.1016/j.bspc.2018.03.007"
}

Jovanović, S., Skorić, T., Sarenac, O., Milutinović-Smiljanić, S., Japundžić-Žigon, N.,& Bajić, D.. (2018). Copula as a dynamic measure of cardiovascular signal interactions. in Biomedical Signal Processing & Control
Elsevier Sci Ltd, Oxford., 43, 250-264.
https://doi.org/10.1016/j.bspc.2018.03.007

Jovanović S, Skorić T, Sarenac O, Milutinović-Smiljanić S, Japundžić-Žigon N, Bajić D. Copula as a dynamic measure of cardiovascular signal interactions. in Biomedical Signal Processing & Control. 2018;43:250-264.
doi:10.1016/j.bspc.2018.03.007 .

Jovanović, Slađana, Skorić, Tamara, Sarenac, Olivera, Milutinović-Smiljanić, Sanja, Japundžić-Žigon, Nina, Bajić, Dragana, "Copula as a dynamic measure of cardiovascular signal interactions" in Biomedical Signal Processing & Control, 43 (2018):250-264,
https://doi.org/10.1016/j.bspc.2018.03.007 . .