XVAE-WMT: Explainable Wavelet-Temporal Variational Autoencoder for Blind Source Separation of Heart and Lung Sounds

2026-08-31

AI Summary

本文提出XVAE-WMT算法，结合变分自编码器与可解释AI技术，通过连续小波变换提高心肺声音盲源分离性能。

Abstract

The separation of cardiovascular sounds is a critical task in biomedical signal processing. In this paper, we introduce XVAE-WMT1, an unsupervised explainable generative AI algorithm combining a variational autoencoder (VAE) with explainable AI (XAI), wavelet-based inputs, a post-hoc output mask, and temporal consistency (TC) loss. Unlike existing supervised and VAE-based methods that rely on Short-Time Fourier Transform (STFT) and ignore latent interpretability, XVAE-WMT requires no paired clean recordings and integrates a Continuous Wavelet Transform (CWT) front-end for superior time-frequency localization. We assessed the latent space interpretability via different metrics, with SHAP (SHapley Additive exPlanations) enabling dimensionality reduction to the top 75% of latent features while preserving separation quality. Evaluated across two datasets using Signal-to-Distortion Ratio (SDR), Signal-to-Interference Ratio (SIR), and Signal-to-Artifacts Ratio (SAR), XVAE-WMT attains 26.8 dB SDR, 32.8 dB SIR, and 28.6 dB SAR.

Problem

• Blind Source Separation
• Heart and Lung Sounds
• Biomedical Signal Processing

Innovation

• XVAE-WMT
• Continuous Wavelet Transform (CWT)
• Temporal Consistency (TC) Loss
• Explainable AI (XAI)
• SHapley Additive exPlanations (SHAP)