  Detrended fluctuation analysis (DFA), suitable for the analysis of
nonstationary time series, has confirmed the existence of persistent long-range
correlations in healthy heart rate variability data. In this paper, we present
the incorporation of the alpha-beta filter to DFA to determine patterns in the
power-law behaviour that can be found in these correlations. Well-known
simulated scenarios and real data involving normal and pathological
circumstances were used to evaluate this process. The results presented here
suggest the existence of evolving patterns, not always following a uniform
power-law behaviour, that cannot be described by scaling exponents estimated
using a linear procedure over two predefined ranges. Instead, the power law is
observed to have a continuous variation with segment length. We also show that
the study of these patterns, avoiding initial assumptions about the nature of
the data, may confer advantages to DFA by revealing more clearly abnormal
physiological conditions detected in congestive heart failure patients related
to the existence of dominant characteristic scales.
