Modelling long-term heart rate variability: an ARFIMA approach

Argentina S. Leite, Ana Paula Rocha, M. Eduarda Silva, Ovídio Costa
2006 Biomedical Engineering  
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
more » ... 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.
doi:10.1515/bmt.2006.040 pmid:17061942 fatcat:6tauxonfybdgln6eipnpnsdsyi