Long-Range Dependence in Heart Rate Variability Data: ARFIMA Modelling vs Detrended Fluctuation Analysis

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2007

Autores

Leite, A (Autor) Outra; Rocha, AP (Autor) FCUP; Silva, ME (Autor) FCUP; Gouveia, S (Autor) Outra; Carvalho, J (Autor) Outra; Costa, O (Autor) Outra

Ata de Conferência Internacional

Título: Computers in Cardiology Series

Páginas: 21-24

34th Annual Conference on Computers in Cardiology

Durham, NC, SEP 30-OCT 03, 2007

Indexação

ISI Web of Knowledge - 11 Citações

Scopus - 19 Citações

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Biotecnologia ambiental

Outras Informações

ID Authenticus: P-004-CVE

DOI: 10.1109/cic.2007.4745411

Abstract (EN): Heart rate variability (HRV) data display non-stationary characteristics and exhibit long-range correlation (memory). Detrended fluctuation analysis (DFA) has become a widely-used technique for long memory estimation in non-stationary HRV data. Recently, we have proposed an alternative approach based on fractional integrated autoregressive moving average (ARFIMA) models. ARFIMA models, combined with selective adaptive segmentation may be used to capture and remove long-range correlation, leading to an improved description and interpretation of tire components in 24 hour HRV recordings. In this work estimation of long memory by DFA and selective adaptive ARFIMA modelling is carried out in 24 hour HRV recordings of 17 healthy subjects of two age groups. The two methods give similar information on long-range global characteristics. However ARFIMA modelling is advantageous, allowing the description of long-range correlation in reduced length segments.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 4