  Preterm labor (PL) has globally become the leading cause of death in children
under the age of 5 years. To address this problem, this paper will provide a
new approach by analyzing the EHG signals, which are recorded on the abdomen of
the mother during labor and pregnancy. The EHG signal reflects the electrical
activity that induces the mechanical contraction of the myometrium. Because
EHGs are known to be non-stationary signals, and because we anticipate
connectivity to alter during contraction, we applied the windowing approach on
real signals to help us identify the best windows and the best nodes with the
most significant data to be used for classification. The suggested pipeline
includes i) divide the 16 EHG signals that are recorded from the abdomen of
pregnant women in N windows; ii) apply the connectivity matrices on each
window; iii) apply the Graph theory-based measures on the connectivity matrices
on each window; iv) apply the consensus Matrix on each window in order to
retrieve the best windows and the best nodes. Following that, several neural
network and machine learning methods are applied to the best windows and best
nodes to categorize pregnancy and labor contractions, based on the different
input parameters (connectivity method alone, connectivity method plus graph
parameters, best nodes, all nodes, best windows, all windows). Results showed
that the best nodes are nodes 8, 9, 10, 11, and 12; while the best windows are
2, 4, and 5. The classification results obtained by using only these best nodes
are better than when using the whole nodes. The results are always better when
using the full burst, whatever the chosen nodes. Thus, the windowing approach
proved to be an innovative technique that can improve the differentiation
between labor and pregnancy EHG signals.
