一个渐进的基于注意力的交叉模式融合网络用于心血管疾病检测,使用同步的心电图和心电图信号
Wei Peng Li1, Joon Huang Chuah1, Guo Jeng Tan2
1Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Kuala Lumpur, Malaysia.
PeerJ. Computer science
|September 24, 2025
概括
这项研究介绍了PACFNet,这是一个新的深度学习网络,用于使用同步心电图 (ECG) 和心电图 (PCG) 信号检测心血管疾病 (CVD). PACFNet通过逐步融合多模式数据来实现最先进的准确性,即使缺少一个信号.
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) 的检测依赖于补充心电图 (ECG) 和心电图 (PCG) 信号.
- 目前的深度学习方法无法充分捕捉模式间的依赖性,限制了心血管疾病检测的准确性.
- 简单的融合策略未能利用ECG和PCG数据之间的等级关系.
研究的目的:
- 引入PACFNet,一个基于注意力的渐进式跨模态特征融合网络,用于端到端的CVD检测.
- 通过逐步整合从低层到高层表示的多模式信息来增强特征学习.
- 通过同步ECG和PCG信号来提高心血管疾病检测的准确性和稳定性.
主要方法:
- 开发了一个三分支架构,用于ECG和PCG的模式特定编码器.
- 实施了四层渐进融合机制,采用了基于注意力的选择性交叉模式融合 (SACMF) 模块.
- 在SACMF中利用级联空间和道注意力,以动态强调跨模式的突出特征.
- 采用节拍对节拍细分来分析个体心脏周期.
主要成果:
- 在PhysioNet 2016数据集上,PACFNet取得了最先进的表现.
- 证明了高精度 (97.7%),灵敏度 (98%),特异性 (97.3%) 和F1得分 (99.7%).
- 保持了强大的诊断能力,即使缺少模式,突出了实际的有效性.
结论:
- PACFNet有效地捕捉了复杂的模式间依赖性,以改善心血管疾病检测.
- 渐进的融合机制增强了特征学习和诊断精度.
- PACFNet为多模式心血管疾病检测提供了可靠和有效的解决方案.
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