图形神经网络在心电图数据上的应用:系统性文献综述
IEEE journal of biomedical and health informatics
|January 12, 2026
概括
几何深度学习 (GDL) 显示出对分析心电图 (ECG) 数据的前景. 本综述强调了图形神经网络 (GNN) 在EGG的GDL中的主导地位,并建议了未来的研究方向.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 几何深度学习 (GDL) 为分析结构化数据提供了新的方法.
- 代表心电图 (ECG) 数据,特别是多导数,由于非欧几里德空间关系而带来了挑战.
- 在电脑电图 (EEG) 分析中成功的图形神经网络 (GNN) 越来越多地应用于ECG.
研究的目的:
- 系统地审查用于ECG数据分析的几何深度学习方法.
- 将现有方法进行比较,并确定研究缺口和未来方向.
- 专注于基于GNN的方法,因为它们在文献中的普遍性.
主要方法:
- 在心电图中对几何深度学习应用的系统文献综述.
- 分析重点是用于ECG信号处理的图形神经网络方法.
- 基于ECG领先使用,应用和网络架构的方法的分类.
主要成果:
- 图形神经网络是ECG分析的主要GDL方法.
- 12导电图的应用程序的多样性有限,主要集中在心律失常的识别上.
- 现有的单线或双线方法表现出高性能,质疑复杂的多线空间建模的附加值对于这个特定的用例.
- 缺乏关于EGG中GDL最佳拓,特征集和网络架构的共识.
结论:
- 未来的研究应该探索12ECG GDL模型的更广泛的应用.
- 需要进一步调查,以确定多空间建模用于心律失常检测的好处.
- 拟议的未来工作包括应用驱动的可解释性研究和理论见解和生理整合的基础研究.
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