无需干净数据，用自监督学习提升心音信号降噪效果
SSC-UNet: UNet with Self-Supervised Contrastive Learning for Phonocardiography Noise Reduction
- 基于Noise2Noise设计自监督模型，无需配对清洁数据
- 在10dB医院噪声下信噪比达12.98dB，病理特征保留率显著提升
- 适合临床心音诊断场景，尤其适用于数据稀缺的医疗环境
先天性心脏病（CHD）是全球范围内影响约1%新生儿的重大健康问题。心音图作为低成本辅助诊断工具，其性能高度依赖信号质量，因此降噪至关重要。传统监督式UNet虽有效，但受限于清洁数据不足；且心音复杂的时频特性使去噪与病理特征保留难以平衡。本研究提出一种基于Noise2Noise的自监督心音降噪模型SSC-UNet，引入增强与对比学习提升性能。在10dB医院噪声环境下，平均信噪比达12.98dB；降噪后分类敏感度从27%提升至88%，验证了其在真实噪声环境中保留病理特征的潜力。
原文摘要 · Abstract (English)
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.
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