Key result
Fixed-length windowing CNN achieves ~80% AUROC for murmur detection, outperforming cycle-normalized CNNs and BEATs models.
Why the study?
Automated classification of phonocardiogram recordings advances cardiovascular diagnostics, but the comparative performance of CNNs versus transformer architectures across different signal normalization approaches remains to be systematically evaluated.
Do specialized CNNs or zero-shot transformer models perform better for automated heart murmur detection from phonocardiograms?
Do specialized CNNs or zero-shot transformer models perform better for automated heart murmur detection from phonocardiograms?
Specialized CNNs with fixed-length windowing demonstrate superior accuracy for automated heart murmur detection compared to zero-shot transformer models, though transformers may offer computational efficiency advantages.
Answers are grounded in this paper and cite their sources.
Does not yet support routine clinical use; leaves open CNN optimization versus transformers in prospective validation.
The automated classification of phonocardiogram (PCG) recordings represents a substantial advancement in cardiovascular diagnostics. This paper presents a systematic comparison of four distinct models for heart murmur detection: two specialized convolutional neural networks (CNNs) and two zero-shot universal audio transformers (BEATs), evaluated using fixed-length and heart cycle normalization approaches. Utilizing the PhysioNet2022 dataset, a custom heart cycle normalization method tailored to individual cardiac rhythms is introduced. The findings indicate the following AUROC values: the CNN model with fixed-length windowing achieves 79.5 %, the CNN model with heart cycle normalization scores 75.4 %, the BEATs transformer with fixed-length windowing achieves 65.7 %, and the BEATs transformer with heart cycle normalization results in 70.1 %.The findings indicate that physiological signal constraints, especially those introduced by different normalization strategies, have a substantial impact on model performance. The research provides evidence-based guidelines for architecture selection in clinical settings, emphasizing the need for a balance between accuracy and computational efficiency. Although specialized CNNs demonstrate superior performance overall, the zero-shot transformer models may offer promising efficiency advantages during development, such as faster training and evaluation cycles, despite their lower classification accuracy. These findings highlight the potential of automated classification systems to enhance cardiac diagnostics and improve patient care.
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Sondermann et al. (2025) studied Heart murmur. Convolutional neural network (CNN) with fixed-length windowing vs. CNN with heart cycle normalization and BEATs transformer models was evaluated on Area under the receiver operating characteristic curve (AUROC) for heart murmur detection. A specialized CNN model with fixed-length windowing achieved an AUROC of 79.5% for heart murmur detection, outperforming CNN with heart cycle normalization (75.4%) and BEATs transformer models.
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