Modelling long-term heart rate variability: an ARFIMA approach

• Tipo: Artigo em Revista Científica Internacional
• Data: 2006

Autores

Argentina S Leite (Autor) Outra; Ana Paula Rocha (Autor) FCUP; Eduarda E Silva (Autor) FCUP; Ovidio Costa (Autor) Outra

Revista

Título: Biomedical Engineering-Biomedizinische Technik

Vol. 51

Páginas: 215-219

ISSN: 0013-5585

Editora: Walter De Gruyter

Indexação

ISI Web of Knowledge - 5 Citações

Scopus - 8 Citações

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Biotecnologia ambiental

Outras Informações

ID Authenticus: P-004-P8F

DOI: 10.1515/bmt.2006.040

Abstract (EN): Long-term heart rate variability (HRV) series can be described by time-variant autoregressive modelling. HRV recordings show dependence between distant observations that is not negligible, suggesting the existence of long-range correlations. In this work, selective adaptive segmentation combined with fractionally integrated autoregressive moving-average models is used to capture long memory in HRV recordings. This approach leads to an improved description of the low- and high-frequency components in HRV spectral analysis. Moreover, it is found that in the 24-h recording of a case report, the long-memory parameter presents a circadian variation, with different regimes for day and night periods.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 5