  Fetal heart rate (FHR) monitoring, before and during labor, is a very
important medical practice in the detection of fetuses in danger. We clustered
FHR tracings by compression in order to identify abnormal ones. We use a
recently introduced approach based on algorithmic information theory, a
theoretical, rigorous and well-studied notion of information content in
individual objects. The new method can mine patterns in completely different
areas, there are no domain-specific parameters to set, and it does not require
specific background knowledge. At the highest level the FHR tracings were
clustered according to an unanticipated feature, namely the technology used in
signal acquisition. At the lower levels all tracings with abnormal or
suspicious patterns were clustered together, independent of the technology
used. Moreover, FHR tracings with future poor neonatal outcomes were included
in the cluster with other suspicious patterns.
