Abstract / Summary
Heart sound classification is crucial in the early detection and diagnosis of cardiovascular diseases, aiming at effectively capturing the complex patterns of heart sound signals. Deep learning models have widespread applications in heart sound classification, but the existing methods solely rely on either early, late or intermediate fusion, which often fail to fully integrate the diverse and multiscale features required for robust classification. To overcome these limitations, this study proposes a novel hierarchical multiscale swin transformer network with attention fusion (HM-Swin), for heart sound classification. The new model incorporates a swin transformer backbone to extract multiscale features spanning from low-level to high-level representations. These features are further refined through a dynamic fusion block, wherein the features extracted by each swin block are enhanced using dedicated channel-wise and spatial attention mechanisms. This approach effectively captures interdependencies, resulting in a robust and comprehensive feature representation. Extensive experiments conducted on publicly available benchmark datasets, namely, PhysioNet2016 and PhysioNet2022, achieve classification accuracies of 98.32% and 90.56%, respectively, demonstrating that HM-Swin achieves higher performance than existing state-of-the-art methods. These results confirm the efficiency and scalability of our model, showing its potential as a solution for automated heart-sound screening and classification.