  Preterm babies in the Neonatal Intensive Care Unit (NICU) have to undergo
continuous monitoring of their cardiac health. Conventional monitoring
approaches are contact-based, making the neonates prone to various nosocomial
infections. Video-based monitoring approaches have opened up potential avenues
for contactless measurement. This work presents a pipeline for remote
estimation of cardiopulmonary signals from videos in NICU setup. We have
proposed an end-to-end deep learning (DL) model that integrates a non-learning
based approach to generate surrogate ground truth (SGT) labels for supervision,
thus refraining from direct dependency on true ground truth labels. We have
performed an extended qualitative and quantitative analysis to examine the
efficacy of our proposed DL-based pipeline and achieved an overall average mean
absolute error of 4.6 beats per minute (bpm) and root mean square error of 6.2
bpm in the estimated heart rate.
