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

2025/02/24 by Han, Jiho, Adnan Shaout, Shaout, Adnan · 2 citations
Medicine · #Artificial Intelligence (cs.AI) #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Phonocardiography and Auscultation Techniques #Sound (cs.SD) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2502.16914

openalex publication_date 2025/02/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Cited by

• AMAuT: A Flexible and Efficient Multiview Audio Transformer Framework Trained from Scratch
• Scattering Transformer: A Training-Free Transformer Architecture for Heart Murmur Detection

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