  In this study, we present a deep learning framework designed to integrate
with our previously developed system that facilitates large-scale 1D fetal
Doppler data collection, aiming to enhance data quality. This system, tailored
for traditional Indigenous midwives in low-resource communities, leverages a
cost-effective Android phone to improve the quality of recorded signals. We
have shown that the Doppler data can be used to identify fetal growth
restriction, hypertension, and other concerning issues during pregnancy.
However, the quality of the signal is dependent on many factors, including
radio frequency interference, position of the fetus, maternal body habitus, and
usage of the Doppler by the birth attendants. In order to provide instant
feedback to allow correction of the data at source, a signal quality metric is
required that can run in real-time on the mobile phone.
  In this study, 191 DUS signals with durations mainly in the range between 5
to 10 minutes were evaluated for quality and classified into five categories:
Good, Poor, (Radiofrequency) Interference, Talking, and Silent, at a resolution
of 3.75 seconds. A deep neural network was trained on each 3.75-second segment
from these recordings and validated using five-fold cross-validation.
  An average micro F1 = 97.4\% and macro F1 = 94.2\% were achieved, with F1 =
99.2\% for `Good' quality data. These results indicate that the algorithm,
which will now be implemented in the midwives' app, should allow a significant
increase in the quality of data at the time of capture.
