Harnessing the Power of Deep Learning Methods in Healthcare: Neonatal Pain Assessment from Crying Sound

Abstract

Neonatal pain assessment in clinical environments is challenging due to its discontinuity and observer subjectivity. Facial and body occlusion is common in such settings for developmental disorder, clinical condition, prone position, and other external factors. In such cases, crying sound can be used to effectively assess neonatal pain. In this paper, we investigate the use of a novel CNN architecture (N-CNN) along with other CNN architectures (VGG16 and ResNet50) for assessing pain from crying sounds of neonates. The experimental results demonstrate that using our novel N-CNN for assessing pain from sounds of neonates has a strong clinical potential and provides a viable alternative to the current assessment practice.