SS-DPPN: A self-supervised dual-path foundation model for the generalizable cardiac audio representation

• URL: http://arxiv.org/abs/2510.10719v1
• Date: Sun, 12 Oct 2025 17:43:57 GMT
• Title: SS-DPPN: A self-supervised dual-path foundation model for the generalizable cardiac audio representation
• Authors: Ummy Maria Muna, Md Mehedi Hasan Shawon, Md Jobayer, Sumaiya Akter, Md Rakibul Hasan, Md. Golam Rabiul Alam,
• Abstract summary: Self-Supervised Dual-Path Prototypical Network (SS-DPPN) is a foundation model for cardiac audio representation and classification from unlabeled data.<n>SS-DPPN achieves state-of-the-art performance on four cardiac audio benchmarks.
• Score: 2.013977297550879
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The automated analysis of phonocardiograms is vital for the early diagnosis of cardiovascular disease, yet supervised deep learning is often constrained by the scarcity of expert-annotated data. In this paper, we propose the Self-Supervised Dual-Path Prototypical Network (SS-DPPN), a foundation model for cardiac audio representation and classification from unlabeled data. The framework introduces a dual-path contrastive learning based architecture that simultaneously processes 1D waveforms and 2D spectrograms using a novel hybrid loss. For the downstream task, a metric-learning approach using a Prototypical Network was used that enhances sensitivity and produces well-calibrated and trustworthy predictions. SS-DPPN achieves state-of-the-art performance on four cardiac audio benchmarks. The framework demonstrates exceptional data efficiency with a fully supervised model on three-fold reduction in labeled data. Finally, the learned representations generalize successfully across lung sound classification and heart rate estimation. Our experiments and findings validate SS-DPPN as a robust, reliable, and scalable foundation model for physiological signals.

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