ENACT-Heart：基于 CNN 与 Transformer 的集成心音分类评估方法
ENACT-Heart -- ENsemble-based Assessment Using CNN and Transformer on Heart Sounds
AI 导读
ENACT-Heart 是一种双分支集成的心音分类方法：Vision Transformer（ViT）处理频谱图图像，CNN 处理波形和谱质心可视化，两者预测通过固定权重的加权平均融合。
正文 · 原文
Abstract:ENACT-Heart is a two-branch ensemble for heart-sound classification. A Vision Transformer (ViT) processes spectrogram images, while a convolutional neural network (CNN) processes visualizations of waveforms and spectral centroids derived from the same recordings. The branch predictions are combined by a weighted average with fixed, input-independent weights. On the PASCAL heart-sound dataset, the ensemble achieved an accuracy of 97.52%, compared with 93.88% for the ViT and 95.45% for the CNN. The ensemble achieved a macro-F1 of 0.94 and recall of 0.74 for extra heart sounds. On this dataset, late fusion improved accuracy and macro-F1 relative to both individual branches.
| Comments: | 7 pages. Revised figures and tables, clarified fixed-weight late-fusion terminology, corrected a reference, moderated claims, and reorganized supporting material into appendices. Added an arXiv change log. No new experiments |
| Subjects: | Sound (cs.SD); Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS) |
| ACM classes: | I.2.6; I.5.4; J.3 |
| Cite as: | arXiv:2502.16914 [cs.SD] |
| (or arXiv:2502.16914v2 [cs.SD] for this version) | |
| https://doi.org/10.48550/arXiv.2502.16914 arXiv-issued DOI via DataCite |
Submission history
From: Jiho Han [view email]
[v1]
Mon, 24 Feb 2025 07:19:28 UTC (1,171 KB)
[v2]
Wed, 4 Nov 2026 06:36:56 UTC (507 KB)
来源：arXiv cs.AI · arxiv.org