arXiv:2502.16914 · v2
ENACT-Heart -- ENsemble-based Assessment Using CNN and Transformer on Heart Sounds
摘要
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.
作者备注：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