SS-DPPN: A self-supervised dual-path foundation model for the generalizable cardiac audio representation
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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of SS-DPPN, a self-supervised dual-path foundation model that effectively learns cardiac audio representations from unlabeled data, demonstrating state-of-the-art performance and significant data efficiency. The innovative methodology and promising experimental results position this work as a valuable addition to the field of machine learning in medical audio analysis.
Methodology
The proposed SS-DPPN framework introduces a self-supervised dual-path architecture that effectively processes both 1D waveforms and 2D spectrograms. The combination of contrastive learning and a hybrid loss function is innovative, particularly in the context of medical audio analysis where labeled data is scarce. The use of a Prototypical Network for metric learning enhances the model's ability to generalize across different tasks, which is a significant methodological advancement. However, the paper could benefit from a more detailed explanation of the hybrid loss function and how it specifically contributes to the model's performance.
Results & evaluation
The experiments are robust, demonstrating state-of-the-art performance across four cardiac audio benchmarks. The paper provides detailed metrics, including validation accuracy and confusion matrices, which offer insights into the model's performance on imbalanced datasets. The inclusion of AUROC and AUPRC curves is commendable, as these metrics are crucial for evaluating performance in medical applications. However, the paper lacks a comparative analysis with other existing models, which would strengthen the claims of superiority.
Reproducibility
The paper does not provide sufficient details regarding the implementation of the SS-DPPN model, such as hyperparameters, training procedures, or data preprocessing steps. This lack of transparency may hinder reproducibility. Including a supplementary material section with code or detailed methodologies would significantly enhance the reproducibility of the results.
Limitations
One identified limitation is the potential overfitting to the specific datasets used for training and validation, particularly given the high performance metrics reported. Additionally, the model's performance on noisy or real-world data is not thoroughly evaluated, which is critical for practical applications in clinical settings. The reliance on synthetic data for some experiments may not fully represent real-world scenarios.
Broader impact
The SS-DPPN model has significant implications for the field of cardiovascular diagnostics, particularly in improving access to automated analysis of phonocardiograms. By reducing the need for labeled data, this approach could enable broader deployment of machine learning solutions in healthcare, potentially leading to earlier diagnosis and better patient outcomes. The generalizability of the model to other physiological signals also opens avenues for further research and application in related fields. The main contribution of this paper is the introduction of SS-DPPN, a self-supervised dual-path foundation model that effectively learns cardiac audio representations from unlabeled data, demonstrating state-of-the-art performance and significant data efficiency. The innovative methodology and promising experimental results position this work as a valuable addition to the field of machine learning in medical audio analysis.
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