IEEE J Biomed Health Inform. 2023 Jun;27(6):2635-2646. doi: 10.1109/JBHI.2022.3215995. Epub 2023 Jun 5.
Stethoscope-recorded chest sounds provide the opportunity for remote cardio-respiratory health monitoring of neonates. However, reliable monitoring requires high-quality heart and lung sounds. This paper presents novel artificial intelligence-based Non-negative Matrix Factorisation (NMF) and Non-negative Matrix Co-Factorisation (NMCF) methods for neonatal chest sound separation. To assess these methods and compare them with existing single-channel separation methods, an artificial mixture dataset was generated comprising heart, lung, and noise sounds. Signal-to-noise ratios were then calculated for these artificial mixtures. These methods were also tested on real-world noisy neonatal chest sounds and assessed based on vital sign estimation error, and a signal quality score of 1-5, developed in our previous works. Overall, both the proposed NMF and NMCF methods outperform the next best existing method by 2.7 dB to 11.6 dB for the artificial dataset, and 0.40 to 1.12 signal quality improvement for the real-world dataset. The median processing time for the sound separation of a 10 s recording was found to be 28.3 s for NMCF and 342 ms for NMF. With the stable and robust performance of our proposed methods, we believe these methods are useful to denoise neonatal heart and lung sounds in the real-world environment.
听诊器记录的胸部声音为远程监测新生儿心肺健康提供了机会。然而，可靠的监测需要高质量的心音和肺音。本文提出了基于人工智能的新非负矩阵分解（NMF）和非负矩阵共同分解（NMCF）方法，用于新生儿胸部声音的分离。为了评估这些方法并将其与现有的单通道分离方法进行比较，我们生成了一个包含心音、肺音和噪声的人工混合数据集。然后计算了这些人工混合物的信噪比。我们还基于先前工作中开发的生命体征估计误差和 1-5 的信号质量评分，在真实的嘈杂新生儿胸部声音上测试了这些方法。总体而言，对于人工数据集，所提出的 NMF 和 NMCF 方法比下一个最佳现有方法的性能分别提高了 2.7 dB 至 11.6 dB，对于真实数据集，信号质量提高了 0.40 至 1.12。对于 10 秒记录的声音分离，NMCF 的中位数处理时间为 28.3 秒，NMF 的中位数处理时间为 342 毫秒。我们相信，由于所提出的方法具有稳定而强大的性能，因此这些方法在现实环境中有助于去除新生儿心音和肺音的噪声。
