| Title | Modelling long-term heart rate variability: an ARFIMA approach. | ||
| Author | Leite, Argentina S; Rocha, Ana Paula; Silva, M Eduarda; Costa, Ovidio | ||
| Journal | Biomed Tech (Berl) | Publication Year/Month | 2006-Oct |
| PMID | 17061942 | PMCID | -N/A- |
| Affiliation | 1.Departamento de Matematica Aplicada, Universidade do Porto, Porto, Portugal. amsleite@fc.up.pt. | ||
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.