A Novel U-Net Architecture for Denoising of Real-world Noise Corrupted Phonocardiogram Signal

TL;DR

This paper introduces a new U-Net based deep learning model for effectively removing real-world noise from phonocardiogram signals, improving diagnostic reliability in heart sound analysis.

Contribution

The paper presents a novel U-Net architecture specifically designed for denoising PCG signals contaminated with real-world noises, validated on synthesized datasets.

Findings

Outperforms existing denoising algorithms quantitatively.

Shows significant improvement in signal clarity and diagnostic accuracy.

Validated on open-access real-world noise and PCG datasets.

Abstract

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.