A New Non-Negative Matrix Co-Factorisation Approach for Noisy Neonatal Chest Sound Separation

TL;DR

This paper introduces a novel Non-negative Matrix Co-Factorisation method for effectively separating noisy neonatal chest sounds into heart, lung, and noise components, improving sound quality and vital sign estimation accuracy.

Contribution

The paper presents a new co-factorisation approach trained on high-quality sounds, outperforming existing NMF methods in noisy neonatal sound separation.

Findings

Significant improvement in sound quality scores

Enhanced accuracy in heart rate estimation by 3.6 bpm

Improved breathing rate estimation by 1.2 bpm

Abstract

Obtaining high-quality heart and lung sounds enables clinicians to accurately assess a newborn's cardio-respiratory health and provide timely care. However, noisy chest sound recordings are common, hindering timely and accurate assessment. A new Non-negative Matrix Co-Factorisation-based approach is proposed to separate noisy chest sound recordings into heart, lung, and noise components to address this problem. This method is achieved through training with 20 high-quality heart and lung sounds, in parallel with separating the sounds of the noisy recording. The method was tested on 68 10-second noisy recordings containing both heart and lung sounds and compared to the current state of the art Non-negative Matrix Factorisation methods. Results show significant improvements in heart and lung sound quality scores respectively, and improved accuracy of 3.6bpm and 1.2bpm in heart and breathing rate estimation respectively, when compared to existing methods.