# Prediction of Neonatal Respiratory Distress in Term Babies at Birth from Digital Stethoscope Recorded Chest Sounds

arXiv: https://arxiv.org/abs/2201.10105 · HTML: https://mlsanity.com/paper/2201.10105/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 25 Jan 2022.

## Builds on (most cited first, 4 of 4)

- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [A New Non-Negative Matrix Co-Factorisation Approach for Noisy Neonatal Chest Sound Separation](https://mlsanity.com/paper/2109.03275/) (2021)
- [Real-Time Multi-Level Neonatal Heart and Lung Sound Quality Assessment for Telehealth Applications](https://mlsanity.com/paper/2109.15127/) (2021)
- [Noisy Neonatal Chest Sound Separation for High-Quality Heart and Lung Sounds](https://mlsanity.com/paper/2201.03211/) (2022)

## Abstract (arXiv)

Neonatal respiratory distress is a common condition that if left untreated, can lead to short- and long-term complications. This paper investigates the usage of digital stethoscope recorded chest sounds taken within 1min post-delivery, to enable early detection and prediction of neonatal respiratory distress. Fifty-one term newborns were included in this study, 9 of whom developed respiratory distress. For each newborn, 1min anterior and posterior recordings were taken. These recordings were pre-processed to remove noisy segments and obtain high-quality heart and lung sounds. The random undersampling boosting (RUSBoost) classifier was then trained on a variety of features, such as power and vital sign features extracted from the heart and lung sounds. The RUSBoost algorithm produced specificity, sensitivity, and accuracy results of 85.0%, 66.7% and 81.8%, respectively.