ARFIMA modeling combined with adaptive segmentation can effectively capture long-range correlations and circadian variations in long-term heart rate variability.
May refine single-case HRV analysis; leaves open generalizability and clinical utility pending larger validation.
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.
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Leite et al. (2006) studied this question.
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