Please use this identifier to cite or link to this item:
http://hdl.handle.net/10773/30281
Title: | Vector Autoregressive Fractionally Integrated models to assess multiscale complexity in cardiovascular and respiratory time series |
Author: | Martins, Aurora Amado, Celestino Rocha, Ana Paula Silva, Maria Eduarda Pernice, Riccardo Javorka, Michal Faes, Luca |
Issue Date: | 15-Jul-2020 |
Publisher: | IEEE |
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. |
Peer review: | yes |
URI: | http://hdl.handle.net/10773/30281 |
DOI: | 10.1109/ESGCO49734.2020.9158136 |
ISBN: | 978-1-7281-5752-8 |
Publisher Version: | https://ieeexplore.ieee.org/document/9158136 |
Appears in Collections: | PSG - Comunicações |
Files in This Item:
File | Description | Size | Format | |
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ESGCO2020_paper_81_final_multivariate_auroraEtal.pdf | 233.13 kB | Adobe PDF |
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