ENACT-Heart -- ENsemble-based Assessment Using CNN and Transformer on Heart Sounds
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 2 of its 15 references that are in the map. First posted on arXiv 24 Feb 2025.
Builds on
2 of 15 references.
References · 2
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Citing papers per year: 2025: 3; 2026: 1.
4 papers.
- 2025Scattering Transformer: A Training-Free Transformer Architecture for Heart Murmur Detectioncited by 1
- 2025AMAuT: A Flexible and Efficient Multiview Audio Transformer Framework Trained from Scratchcited by 1
- 2025HD3C: Efficient Medical Data Classification for Edge Devicescited by 0
- 2026Classification of systolic murmurs in heart sounds using multiresolution complex Gabor dictionary and vision transformercited by 0
- 2026Classification of systolic murmurs in heart sounds using multiresolution complex Gabor dictionary and vision transformer17 Apr 2026
- 2025AMAuT: A Flexible and Efficient Multiview Audio Transformer Framework Trained from Scratch22 Oct 2025
- 2025Scattering Transformer: A Training-Free Transformer Architecture for Heart Murmur Detection22 Sep 2025
- 2025HD3C: Efficient Medical Data Classification for Edge Devices18 Sep 2025
Similar, not cited
Close in wording, no citation either way
- 2024Generative Deep Learning and Signal Processing for Data Augmentation of Cardiac Auscultation Signals: Improving Model Robustness Using Synthetic Audio
- 2025Scaling to Multimodal and Multichannel Heart Sound Classification with Synthetic and Augmented Biosignals
- 2026MARCUS: An agentic, multimodal vision-language model for cardiac diagnosis and management
- 2026Diffusion-Based Heart Sound Generation: Evaluation with Physiological Signal Metrics, Classifiers, and Expert Listening ↗
- 2025EnECG: Efficient Ensemble Learning for Electrocardiogram Multi-task Foundation Model
- 2024Graph Convolutional Neural Networks for Automated Echocardiography View Recognition: A Holistic Approach
- 2026StethoLM: Audio Language Model for Cardiopulmonary Analysis Across Clinical Tasks
- 2022CMKD: CNN/Transformer-Based Cross-Model Knowledge Distillation for Audio Classification