Recently, it has been shown that symmetrical intinsic features of time series derived from  nonlinear dynamical systems could help to detect transitions or changes in operating regimes.

In the same way, entropy rate of m-tuple (APEN, SAMPEN, …) is a relevante descriptor aswell to differentiate biomedical time series when the complexity/irrgularity degree is the discriminating information.

In this presentation, a state of the art of entropy and symmetry concepts is presented. Then, new entropic tools sensitives to different degrees of TRIG (Translation, Reflection, Inversion, Glide) symmetries and a novel descriptor named symmentropy emcopassing the whole symmetrical properties, are developped and tested from synthethic and real (fetal heart rate) biomedical time series.