Vector Autoregressive Fractionally Integrated Models to Assess Multiscale Complexity in Cardiovascular and Respiratory Time Series
- Resource Type
- Authors
- Michal Javorka; Luca Faes; Ana Paula Rocha; Aurora L. R. Martins; Riccardo Pernice; Celestino Amado; Maria Eduarda Silva
- Source
- 2020 11th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO).
- Subject
- Multivariate statistics
vector autoregressive fractionally integrated (VARFI) model
Computer science
Quantitative Biology::Tissues and Organs
Physics::Medical Physics
systolic arterial pressure (SAP)
Cardiovascular variability
computer.software_genre
Correlation
Autoregressive model
multiscale entropy (MSE)
heart period (HP)
Settore ING-INF/06 - Bioingegneria Elettronica E Informatica
Parametric model
Multiple time
Entropy (information theory)
Data mining
Time series
computer
Parametric statistics
- Language
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. published