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

被引：10
|
作者：
Leite, A. [1 ,2 ]
Rocha, A. P. [1 ,2 ]
Silva, M. E. [1 ,3 ]
Gouveia, S. [1 ,2 ]
Carvalho, J. [4 ]
Costa, O. [5 ]
机构：
[1] Univ Porto, Dept Matemat Aplicada, Rua Campo Alegre 687, P-4169007 Oporto, Portugal
[2] Univ Porto, Cent Matemat, P-4100 Porto, Portugal
[3] Univ Aveiro, Unidade Invest Matemat & Aplicac, P-3800 Aveiro, Portugal
[4] Univ Porto, Fac Desporto, CIAFEL, P-4100 Porto, Portugal
[5] Univ Porto, Fac Med, P-4100 Porto, Portugal
来源：
COMPUTERS IN CARDIOLOGY 2007, VOL 34 | 2007年 / 34卷
关键词：
D O I：
10.1109/CIC.2007.4745411
中图分类号：
R5 [内科学];
学科分类号：
1002 ; 100201 ;
摘要：
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.
引用
收藏
页码：21 / +
页数：2
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