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

arXiv: https://arxiv.org/abs/2502.16914 · HTML: https://mlsanity.com/paper/2502.16914/

## 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 (most cited first, 2 of 2)

- [Neural Style Transfer for Audio Spectograms](https://mlsanity.com/paper/1801.01589/) (2018)
- [Deep Learning Based EDM Subgenre Classification using Mel-Spectrogram and Tempogram Features](https://mlsanity.com/paper/2110.08862/) (2021)

## Built on by (most cited first, 4 of 4)

- [Scattering Transformer: A Training-Free Transformer Architecture for Heart Murmur Detection](https://mlsanity.com/paper/2509.18424/) (2025)
- [AMAuT: A Flexible and Efficient Multiview Audio Transformer Framework Trained from Scratch](https://mlsanity.com/paper/2510.19368/) (2025)
- [HD3C: Efficient Medical Data Classification for Edge Devices](https://mlsanity.com/paper/2509.14617/) (2025)
- [Classification of systolic murmurs in heart sounds using multiresolution complex Gabor dictionary and vision transformer](https://mlsanity.com/paper/2604.16563/) (2026)

## 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.