The bio-acoustic information contained within heart sound signals are utilized by physicians world-wide for auscultation purpose. However, the heart sounds are inherently susceptible to noise contamination. Various sources of noises like lung sound, coughing, sneezing, and other background noises are involved in such contamination. Such corruption of the heart sound signal often leads to inconclusive or false diagnosis. To address this issue, we have proposed a novel U-Net based deep neural network architecture for denoising of phonocardiogram (PCG) signal in this paper. For the design, development and validation of the proposed architecture, a novel approach of synthesizing real-world noise corrupted PCG signals have been proposed. For the purpose, an open-access real-world noise sample dataset and an open-access PCG dataset has been utilized. The performance of the proposed denoising methodology has been evaluated on the synthesized noisy PCG dataset. The performance of the proposed algorithm has been compared with existing state-of-the-art (SoA) denoising algorithms qualitatively and quantitatively. The proposed denoising technique has shown improvement in performance as comparison to the SoAs.

翻译：心音信号中蕴含的生物声学信息被全球医生用于听诊目的。然而，心音本身极易受到噪声污染。肺音、咳嗽、打喷嚏及其他背景噪声等多种噪声源均会参与此类污染。这种心音信号的失真常导致不确定或错误的诊断。为解决这一问题，本文提出了一种基于U-Net的新型深度神经网络架构，用于心音图（PCG）信号去噪。为设计、开发及验证所提架构，提出了一种合成真实噪声污染PCG信号的新方法。为此，本研究采用了开源真实噪声样本数据集与开源PCG数据集。所提去噪方法的性能已在合成的含噪PCG数据集上进行了评估。该算法的性能与现有最先进（SoA）去噪算法进行了定性与定量比较。相较于现有SoA算法，所提去噪技术表现出性能的提升。