Cross-Dataset Generalization in Phonocardiogram Murmur Classification: A Benchmark Study of Domain-Conditional Performance and the Role of Multi-Representation Fusion

Background/Objectives: Automated phonocardiogram (PCG) analysis is a candidate tool for scaling up cardiac auscultation, but the internal accuracy reported in many studies may not reflect performance when a model is applied to recordings from a different source. This study evaluated how a broad range of classifiers generalize across PCG datasets and examined whether a multi-representation architecture improves cross-dataset transfer.

Methods

Eleven approaches were trained on the pediatric CirCor DigiScope dataset and evaluated, without target-domain fine-tuning, on two external datasets: a heterogeneous adult/mixed dataset (the PhysioNet/Computing in Cardiology Challenge 2016 database) and a homogeneous five-class open-access heart sound dataset. The approaches spanned classical machine learning (spport vector machine, random forest), standard deep models (1D-CNN, 2D-CNN, ResNet1D, CNN-BiLSTM, and two hybrid networks), image-based backbones (EfficientNet-B0, MaxViT), and a proposed four-branch fusion model (PCG-MambaConformer) combining raw-signal, mel-spectrogram, and continuous wavelet transform (CWT) representations. Threshold-transfer and precision–recall analyses, adversarial domain adaptation, an uncertainty-quantification benchmark, a disease-specific transfer analysis, multi-architecture ensembling, a branch ablation, a label-grouping sensitivity analysis, a reciprocal (reverse-direction) transfer experiment and explainability analyses (feature-space t-SNE with class-separability metrics and Grad-CAM saliency) were also performed. Performance was summarized using the area under the receiver operating characteristic curve (AUROC) with sensitivity and specificity.