A novel segmentation algorithm reveals that while the scale-invariant structure of heart rate nonstationarity is preserved in heart failure, the magnitude of mean heart rate jumps between segments is reduced, suggesting a common functional form with altered parameters in disease.
Reduced jumps may signal blunted HF regulation; extends scale-free HR analysis but remains hypothesis-generating.
We introduce a segmentation algorithm to probe the temporal organization of heterogeneities in human heartbeat interval time series. We find that the lengths of segments with different local mean heart rates follow a power-law distribution and show that this scale-invariant structure is not a simple consequence of the long-range correlations present in the data. The differences in mean heart rates between consecutive segments display a common functional form, but with different parameters for healthy individuals and for heart-failure patients. These findings suggest that there is relevant physiological information hidden in the heterogeneities of the heartbeat time series.
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Bernaola‐Galván et al. (2001) studied this question.
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