  Electric field variations that appear before rupture have been recently
studied by employing the detrended fluctuation analysis (DFA) as a scaling
method to quantify long-range temporal correlations. These studies revealed
that seismic electric signals (SES) activities exhibit a scale invariant
feature with an exponent $\alpha_{DFA} \approx 1$ over all scales investigated
(around five orders of magnitude). Here, we study what happens upon significant
data loss, which is a question of primary practical importance, and show that
the DFA applied to the natural time representation of the remaining data still
reveals for SES activities an exponent close to 1.0, which markedly exceeds the
exponent found in artificial (man-made) noises. This, in combination with
natural time analysis, enables the identification of a SES activity with
probability 75% even after a significant (70%) data loss. The probability
increases to 90% or larger for 50% data loss.
