ENACT-Heart -- ENsemble-based Assessment Using CNN and Transformer on Heart Sounds

This study explores the application of Vision Transformer (ViT) principles in audio analysis, specifically focusing on heart sounds. This paper introduces ENACT-Heart - a novel ensemble approach that leverages the complementary strengths of Convolutional Neural Networks (CNN) and ViT through a Mixture of Experts (MoE) framework, achieving a remarkable classification accuracy of 97.52%. This outperforms the individual contributions of ViT (93.88%) and CNN (95.45%), demonstrating the potential for enhanced diagnostic accuracy in cardiovascular health monitoring. These results demonstrate the potential of ensemble methods in enhancing classification performance for cardiovascular health monitoring and diagnosis.

@article{han2025_2502.16914,
  title={ ENACT-Heart -- ENsemble-based Assessment Using CNN and Transformer on Heart Sounds },
  author={ Jiho Han and Adnan Shaout },
  journal={arXiv preprint arXiv:2502.16914},
  year={ 2025 }
}