  Complications during pregnancy and labor are common and can be especially
detrimental in populations with limited access to healthcare. A promising
technology to address these complications is the electrohysterogram (EHG),
which measures abdominal electric potentials. Since EHG recordings are measured
noninvasively, they record uterine electrical activity together with activity
from other interfering sources, making their analysis more challenging. To
facilitate the analysis of EHGs, we separate these measurements into uterine
activity that is more variable across different electrodes and over time, which
we term localized activity, and activity that is more evenly distributed in
space and time. We represent multi-electrode EHGs as tensors and develop a
Bayesian tensor decomposition for estimating localized and distributed
electrical activities. To demonstrate that our method can estimate localized
and distributed activities more accurately than existing methods, we simulate
EHG measurements based on previous characterizations of the activities
comprising these measurements. Furthermore, we demonstrate the effectiveness of
our method in separating EHG bursts from other interfering activities recorded
in EHGs using real measurements from two public datasets. Our results show that
our method denoises EHG bursts with higher signal-to-noise ratios, defined as
power ratios between EHG bursts and segments with only baseline EHG activity,
than alternative methods. Such accurate estimation of uterine and other
activities from EHG measurements could be useful for more precisely
characterizing patterns of activity in EHGs, which is a step forward towards
developing reliable, portable devices to reduce risks during pregnancy and
labor.
