arXiv 2507.070582025-07-08cs.SDcs.AIcs.LGeess.AS

带有心周期归一化的CNN和Transformer架构比较用于自动心音图分类

Comparative Analysis of CNN and Transformer Architectures with Heart Cycle Normalization for Automated Phonocardiogram Classification

Fraunhofer Institute for Software and Systems Engineering ISST（弗劳恩霍夫软件与系统工程研究所）；Department of Computer Science, University of Applied Sciences and Arts Dortmund（应用科学大学多特蒙德计算机科学系）；Department of Computer Science, TU Dortmund University（图尔努恩大学计算机科学系）；Department of Health Informatics, School of Medicine, Witten/Herdecke University（医学学院健康信息学系，维滕/赫尔德克大学）；Institute for Medical Informatics, Biometry and Epidemiology (IMIBE), University Hospital Essen（医学信息学、生物统计与流行病学研究所（IMIBE），埃森大学医院）

Martin Sondermann, Pinar Bisgin, Niklas Tschorn, Anja Burmann, Christoph M. Friedrich

备注：: Preprint Version. Accepted at EMBC 2025
DOI：: 10.1109/EMBC58623.2025.11251770

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AI总结：

本文比较了带有心周期归一化的CNN和Transformer架构在心音图分类中的性能，发现CNN在准确性上更优，但Transformer在效率上更具优势。

摘要：

自动心音图（PCG）记录的分类代表了心血管诊断的重要进展。本文系统比较了四种不同的模型用于心杂音检测：两个专门的卷积神经网络（CNN）和两个零样本通用音频变换器（BEATs），并使用固定长度和心周期归一化方法进行评估。利用PhysioNet2022数据集，提出了一种针对个体心脏节律定制的心周期归一化方法。研究结果表明，固定长度窗口的CNN模型达到79.5%的AUROC值，带有心周期归一化的CNN模型得分为75.4%，固定长度窗口的BEATs变换器达到65.7%，而带有心周期归一化的BEATs变换器得分为70.1%。研究结果表明，生理信号约束，特别是不同归一化策略引入的约束，对模型性能有显著影响。研究为临床环境中的架构选择提供了证据支持的指南，强调了准确性和计算效率之间的平衡的必要性。尽管专门设计的CNN在整体性能上更优，但零样本变换器模型在开发过程中可能提供有前景的效率优势，例如更快的训练和评估周期，尽管其分类准确性较低。这些发现突显了自动化分类系统在增强心脏诊断和改善患者护理方面的潜力。

英文摘要：

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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