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

TL;DR

This paper explores using deep learning, specifically a novel CNN architecture, to assess neonatal pain from crying sounds, offering a promising alternative to traditional facial and body-based methods in clinical settings.

Contribution

Introduction of a new CNN architecture (N-CNN) for neonatal pain assessment from crying sounds, demonstrating its effectiveness compared to existing CNN models.

Findings

N-CNN outperforms VGG16 and ResNet50 in pain assessment accuracy.

Crying sound analysis provides a viable alternative when facial/body cues are occluded.

Deep learning models show strong potential for clinical neonatal pain evaluation.

Abstract

Neonatal pain assessment in clinical environments is challenging as it is discontinuous and biased. Facial/body occlusion can occur in such settings due to clinical condition, developmental delays, prone position, or 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 the sounds of neonates has a strong clinical potential and provides a viable alternative to the current assessment practice.