Vector Autoregressive Fractionally Integrated Models to Assess Multiscale Complexity in Cardiovascular and Respiratory Time Series

Riccardo Pernice; Luca Faes; Maria Eduarda Silva; Michal Javorka; Aurora Martins; Celestino Amado; Ana Paula Rocha

Vector Autoregressive Fractionally Integrated Models to Assess Multiscale Complexity in Cardiovascular and Respiratory Time Series

Riccardo Pernice, Luca Faes, Maria Eduarda Silva, Michal Javorka, Aurora Martins, Celestino Amado, Ana Paula Rocha

Ingegneria

Risultato della ricerca: Conference contribution

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.

Lingua originale	English
Titolo della pubblicazione ospite	2020 11th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO)
Pagine	1-2
Numero di pagine	2
Stato di pubblicazione	Published - 2020

All Science Journal Classification (ASJC) codes

Computer Science Applications
Modelling and Simulation
Cardiology and Cardiovascular Medicine
Health Informatics
Physiology (medical)
Instrumentation

Altri file e collegamenti

OA Link

Fingerprint Entra nei temi di ricerca di 'Vector Autoregressive Fractionally Integrated Models to Assess Multiscale Complexity in Cardiovascular and Respiratory Time Series'. Insieme formano una fingerprint unica.

Cita questo

Vector Autoregressive Fractionally Integrated Models to Assess Multiscale Complexity in Cardiovascular and Respiratory Time Series. / Pernice, Riccardo ; Faes, Luca; Silva, Maria Eduarda; Javorka, Michal; Martins, Aurora; Amado, Celestino; Rocha, Ana Paula.

2020 11th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO). 2020. pag. 1-2.

Risultato della ricerca: Conference contribution

@inproceedings{eff4b3a3d6d14170a4cb37cf9a833f8a,

title = "Vector Autoregressive Fractionally Integrated Models to Assess Multiscale Complexity in Cardiovascular and Respiratory Time Series",

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.",

author = "Riccardo Pernice and Luca Faes and Silva, {Maria Eduarda} and Michal Javorka and Aurora Martins and Celestino Amado and Rocha, {Ana Paula}",

year = "2020",

language = "English",

isbn = "978-1-7281-5751-1",

pages = "1--2",

booktitle = "2020 11th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO)",

}

TY - GEN

T1 - Vector Autoregressive Fractionally Integrated Models to Assess Multiscale Complexity in Cardiovascular and Respiratory Time Series

AU - Pernice, Riccardo

AU - Faes, Luca

AU - Silva, Maria Eduarda

AU - Javorka, Michal

AU - Martins, Aurora

AU - Amado, Celestino

AU - Rocha, Ana Paula

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10447/430394

M3 - Conference contribution

SN - 978-1-7281-5751-1

SP - 1

EP - 2

BT - 2020 11th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO)

ER -