H-LDM: Hierarchical Latent Diffusion Models for Controllable and Interpretable PCG Synthesis from Clinical Metadata

• URL: http://arxiv.org/abs/2511.14312v1
• Date: Tue, 18 Nov 2025 10:16:22 GMT
• Title: H-LDM: Hierarchical Latent Diffusion Models for Controllable and Interpretable PCG Synthesis from Clinical Metadata
• Authors: Chenyang Xu, Siming Li, Hao Wang,
• Abstract summary: H-LDM is a Hierarchical Latent Diffusion Model for generating clinically accurate and controllable PCG signals from structured metadata.<n>Our approach features a multi-scale VAE that learns a physiologically-disentangled latent space, separating rhythm, heart sounds, and murmurs.<n> Experiments on the PhysioNet CirCor dataset demonstrate state-the-art performance, achieving a Fréchet Audio Distance of 9.7, a 92% attribute disentanglement score, and 87.1% clinical validity confirmed by cardiologists.
• Score: 7.158541967057649
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Phonocardiogram (PCG) analysis is vital for cardiovascular disease diagnosis, yet the scarcity of labeled pathological data hinders the capability of AI systems. To bridge this, we introduce H-LDM, a Hierarchical Latent Diffusion Model for generating clinically accurate and controllable PCG signals from structured metadata. Our approach features: (1) a multi-scale VAE that learns a physiologically-disentangled latent space, separating rhythm, heart sounds, and murmurs; (2) a hierarchical text-to-biosignal pipeline that leverages rich clinical metadata for fine-grained control over 17 distinct conditions; and (3) an interpretable diffusion process guided by a novel Medical Attention module. Experiments on the PhysioNet CirCor dataset demonstrate state-of-the-art performance, achieving a Fréchet Audio Distance of 9.7, a 92% attribute disentanglement score, and 87.1% clinical validity confirmed by cardiologists. Augmenting diagnostic models with our synthetic data improves the accuracy of rare disease classification by 11.3\%. H-LDM establishes a new direction for data augmentation in cardiac diagnostics, bridging data scarcity with interpretable clinical insights.

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