TF-crossnet:使用pcg和ecg信号进行心血管疾病分类的跨模式注意力融合网络
Xingguang Li1, Yutong Hou1, Kaiyao Shi2
1College of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, People's Republic of China.
Biomedical physics & engineering express
|November 3, 2025
概括
一个新的AI网络,TF-CrossNet,通过融合心电图 (ECG) 和心声图 (PCG) 信号来提高早期心血管疾病 (CVD) 诊断. 这种方法提高了非侵入性心血管疾病查的准确性和稳定性.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 电心电图 (ECG) 和声心电图 (PCG) 是心血管疾病 (CVD) 查的重要非侵入性工具.
- 目前的方法面临的挑战是,在多式联运信号交互和重量分配,以实现最佳的心血管疾病检测.
研究的目的:
- 引入一个新的时间频率跨模态注意力融合网络 (TF-CrossNet) 进行精确的早期心血管疾病诊断.
- 加强ECG和PCG信号的融合,以改善心血管疾病分类.
主要方法:
- 利用双路径的多级残余结构从ECG和PCG信号中提取时间频率特征.
- 实施了一种双向互增强的注意模块,以捕获跨模式的形态信息.
- 采用了基于贝叶斯决策理论的自适应融合策略,用于动态模态重量优化.
主要成果:
- 在PhysioNet/CinC数据集上实现了高性能:93.13%的准确性,97.7%的特异性和98%的AUC.
- 与现有方法相比,证明了优越的噪声强度,平均强度指数为94.20%.
- 通过全面的噪音实验,验证了在临床环境中的实际应用性.
结论:
- TF-CrossNet为非侵入性和精确的心血管疾病诊断提供了一条新的技术途径.
- 拟议的网络有效地解决了多式联运互动和重量分配的局限性,以加强心血管疾病查.
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