ENACT-Heart -- ENsemble-based Assessment Using CNN and Transformer on Heart Sounds
To address the limitation of insufficient heart sound classification accuracy in cardiovascular intelligent diagnosis, this paper proposes a Mixture-of-Experts (MoE) ensemble framework integrating Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). It is the first work to establish a CNN–ViT collaborative MoE paradigm for heart sound analysis, effectively leveraging the complementary strengths of CNNs in capturing local time-frequency patterns and ViTs in modeling long-range temporal dependencies. Evaluated on the standard four-class heart sound classification task, the proposed model achieves 97.52% accuracy—outperforming standalone CNN (95.45%) and ViT (93.88%) baselines by 2.07% and 3.64%, respectively. This work introduces a novel multimodal modeling approach for bioacoustic signals and advances the development of interpretable, robust automated cardiovascular health monitoring systems.