  We propose an approach for analyzing signals with long-range correlations by
decomposing the signal increment series into magnitude and sign series and
analyzing their scaling properties. We show that signals with identical
long-range correlations can exhibit different time organization for the
magnitude and sign. We find that the magnitude series relates to the nonlinear
properties of the original time series, while the sign series relates to the
linear properties. We apply our approach to the heartbeat interval series and
find that the magnitude series is long-range correlated, while the sign series
is anticorrelated and that both magnitude and sign series may have clinical
applications.
