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

• Type: Article in International Conference Proceedings Book
• Date: 2007

Authors

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

Conference proceedings International

Title: Computers in Cardiology Series

Pages: 21-24

34th Annual Conference on Computers in Cardiology

Durham, NC, SEP 30-OCT 03, 2007

Indexing

ISI Web of Knowledge - 11 Citations

Scopus - 19 Citations

Scientific classification

FOS: Engineering and technology > Environmental biotechnology

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 4