双模电心电图和PCG心血管疾病检测:探索潜在和模式贡献
Alessia Calzoni1,2, Mattia Savardi3, Marco Silvestri4
1University of Brescia, Department of Information Engineering, Via Branze 38, Brescia, 25123, Italy. alessia.calzoni@unibs.it.
Journal of medical systems
|September 12, 2025
概括
这项研究引入了一种新的深度学习模型,将心电图 (ECG) 和心电图 (PCG) 信号结合起来,用于早期检测心血管疾病 (CVD). 与单一模式方法相比,双模式方法显著提高了诊断准确性.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 心脏病学 心脏病学
背景情况:
- 早期发现心血管疾病 (CVD) 对患者的治疗结果和降低医疗保健成本至关重要.
- 电心电图 (ECG) 和声心电图 (PCG) 是成本效益高,非侵入性的心脏病查工具.
- 双模 (ECG+PCG) 数据集的有限可用性阻碍了先进的诊断模型的开发.
研究的目的:
- 开发和评估一个新的双模深度学习模型,集成ECG和PCG信号,以提高早期CVD检测.
- 通过利用预先训练的模型和公开可用的单模数据集来应对有限的双模数据的挑战.
- 解释模型的决策过程,并可视化正常和病态样本之间的特征分离.
主要方法:
- 提出了一种双模深度学习架构,其中包括一个微调的音频预训练CNN (PCG) 和1D-CNN (ECG) 的晚期融合.
- 使用所有可用的单模PCG数据集,对PCG分支进行了微调.
- 该模型在增强版本的MITHSDB数据集上进行了评估,使用可解释性技术和UMAP用于可视化.
主要成果:
- 双模模型实现了96.4%的接收器运行特征曲线 (AUROC) 下面面积,超过了仅ECG (约. 93.4%) 和仅使用PCG (约. 85.4%) 的模型.
- 可解释性方法量化了电气和声学特征的贡献.
- 通过UMAP可视化,正常心脏样本和病态心脏样本之间有明显的分离.
结论:
- 通过双模深度学习方法将ECG和PCG信号结合起来,可以显著提高早期心血管疾病检测的准确性.
- 可解释性和可视化技术为模型的性能和功能贡献提供了宝贵的见解.
- 该研究强调了多式联通数据融合在心血管疾病诊断方面的潜力,并强调了需要更大,更多样化的双式联通数据集的需要.
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