用变分自编码器无监督分离心肺音，无需标签也能精准还原。
Blind Source Separation in Biomedical Signals Using Variational Methods
- 基于变分自编码器学习混合信号的结构化潜在表示。
- 在真实临床数据上实现心音与肺音的清晰聚类与高保真重建。
- 适合开发便携式诊断设备和智能听诊器系统。
本研究提出一种新颖的无监督方法，利用变分自编码器（VAEs）分离重叠的心音与肺音。在临床环境中，这些声音常相互干扰，人工分离困难且易出错。所提模型通过编码器将混合信号映射至结构化的潜在空间，并使用概率解码器重建各成分，整个过程无需标注数据或源信号先验知识。该方法应用于数字听诊器采集的临床模拟人真实录音数据。结果表明，潜在空间中存在明显对应心音与肺音的聚类，且重建信号能有效保留原始信号的关键频谱特征。该方法为盲源分离提供了鲁棒且可解释的解决方案，具有在便携式诊断工具与智能听诊系统中的应用潜力。
原文摘要 · Abstract (English)
This study introduces a novel unsupervised approach for separating overlapping heart and lung sounds using variational autoencoders (VAEs). In clinical settings, these sounds often interfere with each other, making manual separation difficult and error-prone. The proposed model learns to encode mixed signals into a structured latent space and reconstructs the individual components using a probabilistic decoder, all without requiring labeled data or prior knowledge of source characteristics. We apply this method to real recordings obtained from a clinical manikin using a digital stethoscope. Results demonstrate distinct latent clusters corresponding to heart and lung sources, as well as accurate reconstructions that preserve key spectral features of the original signals. The approach offers a robust and interpretable solution for blind source separation and has potential applications in portable diagnostic tools and intelligent stethoscope systems.
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