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

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2020

Autores

Martins, A (Autor) Outra; Amado, C (Autor) Outra; Rocha, AP (Autor) FCUP; Maria Eduarda Silva (Autor) FEP; Pernice, R (Autor) Outra; Javorka, M (Autor) Outra; Faes, L (Autor) Outra

Ata de Conferência Internacional

Título: 2020 11th Conference of the European Study Group on Cardiovascular Oscillations: Computation and Modelling in Physiology: New Challenges and Opportunities, ESGCO 2020

11th Conference of the European Study Group on Cardiovascular Oscillations, ESGCO 2020

15 July 2020

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00S-S4W

DOI: 10.1109/esgco49734.2020.9158136

Abstract (EN): 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. © 2020 IEEE.

Idioma: Inglês

Tipo (Avaliação Docente): Científica