通过双模ViT集成ECG和PCG信号,用于检测冠状动脉疾病
IEEE journal of biomedical and health informatics
|July 15, 2025
概括
一个新的AI模型CAD-ViT,有效地使用心电图 (ECG) 和心电图 (PCG) 信号选冠状动脉疾病 (CAD). 这种非侵入性方法显示出高准确性,改善了对这一主要死亡原因的早期检测.
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:
- 心血管疾病 (CVD),特别是冠状动脉疾病 (CAD),是全球主要的健康问题,也是导致死亡的主要原因.
- 目前的CAD查方法往往缺乏准确性,非侵入性或成本效益,突出了需要改进诊断工具的需要.
研究的目的:
- 开发和评估一种新的,非侵入性分类框架,用于准确检测冠状动脉疾病 (CAD).
- 整合心电图 (ECG) 和心电图 (PCG) 信号,使用先进的AI模型提高诊断性能.
主要方法:
- 一个基于视觉变压器的框架,共同注意双模 ViT (CAD-ViT),旨在处理ECG和PCG信号.
- 关键的创新包括用于跨模式特征交互的Co-Attention机制和用于自适应信号集成的动态加权融合 (DWF) 模块.
- 该模型在一个私人临床数据集和两个公共数据集上进行了验证.
主要成果:
- 在私有数据集上,CAD-ViT实现了高性能,准确率为97.08%,精度为97.18%,特异性为98.52%,F1得分为97.04,回忆率为96.94%.
- 对公共数据集的验证证实了该模型在CAD检测中的稳定性和概括能力.
- 共同注意力和动态融合机制有效地利用了来自ECG和PCG信号的互补信息.
结论:
- 拟议的CAD-ViT框架表明,对冠状动脉疾病 (CAD) 的准确和非侵入性查有很大的潜力.
- 使用先进的人工智能技术的多模式信号集成为改善心血管疾病诊断提供了一个有希望的途径.
- 该模型的高性能表明,在CAD查中实践临床部署的可行性.
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