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

arXiv: https://arxiv.org/abs/1909.02543 · HTML: https://mlsanity.com/paper/1909.02543/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2019.

## Built on by (most cited first, 3 of 3)

- [Pattern Recognition in Vital Signs Using Spectrograms](https://mlsanity.com/paper/2108.03168/) (2021)
- [Securing AI-based Healthcare Systems using Blockchain Technology: A State-of-the-Art Systematic Literature Review and Future Research Directions](https://mlsanity.com/paper/2206.04793/) (2022)
- [Exploring Remote Photoplethysmography for Neonatal Pain Detection from Facial Videos](https://mlsanity.com/paper/2604.25680/) (2026)

## Abstract (arXiv)

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.