SSC-UNet: UNet with Self-Supervised Contrastive Learning for Phonocardiography Noise Reduction

TL;DR

This paper introduces SSC-UNet, a self-supervised contrastive learning model for phonocardiography noise reduction that improves diagnostic feature preservation without requiring clean training data.

Contribution

It proposes a novel self-supervised Noise2Noise-based UNet model with augmentation and contrastive learning for effective noise reduction in phonocardiography.

Findings

Achieved an average SNR of 12.98 dB after filtering.

Improved classification sensitivity from 27% to 88%.

Demonstrated effective noise reduction in noisy hospital environments.

Abstract

Congenital Heart Disease (CHD) remains a significant global health concern affecting approximately 1\% of births worldwide. Phonocardiography has emerged as a supplementary tool to diagnose CHD cost-effectively. However, the performance of these diagnostic models highly depends on the quality of the phonocardiography, thus, noise reduction is particularly critical. Supervised UNet effectively improves noise reduction capabilities, but limited clean data hinders its application. The complex time-frequency characteristics of phonocardiography further complicate finding the balance between effectively removing noise and preserving pathological features. In this study, we proposed a self-supervised phonocardiography noise reduction model based on Noise2Noise to enable training without clean data. Augmentation and contrastive learning are applied to enhance its performance. We obtained an average SNR of 12.98 dB after filtering under 10~dB of hospital noise. Classification sensitivity after filtering was improved from 27\% to 88\%, indicating its promising pathological feature retention capabilities in practical noisy environments.