Modelling long-term heart rate variability: an ARFIMA approach.
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.
Leite
Argentina S
AS
Departamento de Matemática Aplicada, Universidade do Porto, Porto, Portugal. amsleite@fc.up.pt
Rocha
Ana Paula
AP
Silva
M Eduarda
ME
Costa
Ovídio
O
D000222:Adaptation, Physiological / Q000502:physiology; D000328:Adult; D000465:Algorithms; D001683:Biological Clocks / Q000502:physiology*; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D015716:Electrocardiography, Ambulatory / Q000379:methods*; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D008297:Male; D008955:Models, Cardiovascular