  Digital stethoscopes in combination with telehealth allow chest sounds to be
easily collected and transmitted for remote monitoring and diagnosis. Chest
sounds contain important information about a newborn's cardio-respiratory
health. However, low-quality recordings complicate the remote monitoring and
diagnosis. In this study, a new method is proposed to objectively and
automatically assess heart and lung signal quality on a 5-level scale in
real-time and to assess the effect of signal quality on vital sign estimation.
For the evaluation, a total of 207 10s long chest sounds were taken from 119
preterm and full-term babies. Thirty of the recordings from ten subjects were
obtained with synchronous vital signs from the Neonatal Intensive Care Unit
(NICU) based on electrocardiogram recordings. As reference, seven annotators
independently assessed the signal quality. For automatic quality
classification, 400 features were extracted from the chest sounds. After
feature selection using minimum redundancy and maximum relevancy algorithm,
class balancing, and hyper-parameter optimization, a variety of multi-class and
ordinal classification and regression algorithms were trained. Then, heart rate
and breathing rate were automatically estimated from the chest sounds using
adapted pre-existing methods. The results of subject-wise leave-one-out
cross-validation show that the best-performing models had a mean squared error
(MSE) of 0.49 and 0.61, and balanced accuracy of 57% and 51% for heart and lung
qualities, respectively. The best-performing models for real-time analysis
(<200ms) had MSE of 0.459 and 0.67, and balanced accuracy of 57% and 46%,
respectively. Our experimental results underscore that increasing the signal
quality leads to a reduction in vital sign error, with only high-quality
recordings having a mean absolute error of less than 5 beats per minute, as
required for clinical usage.
