English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The bio-acoustic information contained within heart sound signals areutilized by physicians world-wide for auscultation purpose. However, the heartsounds are inherently susceptible to noise contamination. Various sources ofnoises like lung sound, coughing, sneezing, and other background noises areinvolved in such contamination. Such corruption of the heart sound signal oftenleads to inconclusive or false diagnosis. To address this issue, we haveproposed a novel U-Net based deep neural network architecture for denoising ofphonocardiogram (PCG) signal in this paper. For the design, development andvalidation of the proposed architecture, a novel approach of synthesizingreal-world noise corrupted PCG signals have been proposed. For the purpose, anopen-access real-world noise sample dataset and an open-access PCG dataset hasbeen utilized. The performance of the proposed denoising methodology has beenevaluated on the synthesized noisy PCG dataset. The performance of the proposedalgorithm has been compared with existing state-of-the-art (SoA) denoisingalgorithms qualitatively and quantitatively. The proposed denoising techniquehas shown improvement in performance as comparison to the SoAs.

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