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Pregnancy monitoring using graph theory based analysis

Monitoring pregnancy using noninvasive recordings of uterine contractions is still an unsolved issue. Here, we propose a new way to tackle this problem using the electrohysterographic (EHG) signals recorded during pregnancy and labor. The new approach is based on the analysis of the propagation of the uterine electrical activity. The proposed pipeline includes i) the computation of the statistical dependencies between the multichannel (4 × 4 matrix) EHG signals, ii) the characterization of the connectivity matrices using network measures (graph-theory based analysis) and iii) the use of these measures in pregnancy monitoring. Due to its robustness to volume conduction, we used the imaginary part of coherence function to create the connectivity matrices transformed then into graphs. The method is evaluated on a dataset of EHG signals to track the correlation between uterine signals with weeks before labor. The results show a difference in the graph densities from pregnancy to labor. We speculate that the network based analysis is a very promising tool for pregnancy monitoring.