HPA-Net: a lightweight hierarchical progressive fusion network for cardiac disease detection based on ECG-PCG signals.

Cardiovascular disease (CVD) remains a leading global health threat, creating a strong clinical need for convenient and rapid diagnostic methods. However, current single-modality and conventional fusion methods often fail to adequately utilize the intrinsic bioelectromechanical coupling characteristics of the heart. To address these limitations, we propose the Hierarchical Progressive Attention Fusion Network (HPA-Net), a lightweight, dual-modal framework designed for efficient and reliable diagnostic performance on resource-constrained devices. Approach: HPA-Net is a compact deep learning architecture comprising only 0.21 million parameters and 0.362G FLOPs. It utilizes a multi-scale convolutional attention encoder to extract spatiotemporal representations from electrocardiogram (ECG) and phonocardiogram (PCG) signals. A multi-level strategy was employed to integrate the features across multiple processing stages. Furthermore, the Fast Adaptive Multitask Optimization (FAMO) technique was implemented to strengthen multi-branch learning and mitigate the issue of modality dominance during the training process. Main results: Rigorous validation on the PhysioNet/CinC 2016 Subset-A dataset demonstrated that HPA-Net achieved an accuracy of 97.59 ± 0.72%, sensitivity of 98.42 ± 0.46%, and specificity of 95.53 ± 1.87%, achieving highly competitive accuracy with drastically reduced computational overhead. Despite its compact size, the model exhibited strong generalization, reaching 97% accuracy on the independent unimodal datasets. Technical evaluation showed an inference latency of 41.20 ms and a Real-Time Factor (RTF) of 0.008. Significance: The proposed architecture efficiently acquires complex spatiotemporal representations while maintaining a minimal computational demand. Its high accuracy combined with low inference latency confirms the feasibility of HPA-Net for seamless deployment on edge hardware. providing a promising solution for real-time cardiovascular monitoring and early disease detection in practical clinical or home-care scenarios.