MedGNN：面向医学时间序列分类的多分辨率时空图学习
MedGNN: Towards Multi-resolution Spatiotemporal Graph Learning for Medical Time Series Classification
AI总结：
MedGNN提出多分辨率自适应图、差分注意力网络、频率卷积网络和多分辨率图Transformer，以解决医学时间序列分类中多尺度时空依赖、基线漂移和多视图建模问题，并在多个真实医学数据集上取得优越性能。
摘要：
医学时间序列在现实世界医疗系统中发挥着至关重要的作用，是监测患者健康状况的重要信息。对医学时间序列（例如心电图（ECG）信号）进行准确分类有助于早期检测和诊断。传统的医学时间序列分类方法依赖于手工特征提取和统计方法；随着人工智能的最新进展，机器学习和深度学习方法变得更加流行。然而，现有方法往往无法充分建模不同尺度下的复杂空间动态，忽略了动态的多分辨率时空联合相互依赖关系。此外，它们较少考虑特殊的基线漂移问题以及医学时间序列的多视图特性，这在很大程度上阻碍了其预测性能。为了解决这些局限性，我们提出了一种多分辨率时空图学习框架 MedGNN，用于医学时间序列分类。具体来说，我们首先提出构建多分辨率自适应图结构来学习动态多尺度嵌入。然后，为了解决基线漂移问题，我们提出差分注意力网络（Difference Attention Networks），在有限差分上执行自注意力机制以进行时间建模。此外，为了学习多视图特性，我们利用频率卷积网络（Frequency Convolution Networks）从频域捕获医学时间序列的互补信息。另外，我们引入了多分辨率图 Transformer 架构来建模动态依赖关系并融合来自不同分辨率的信息。最后，我们在多个医学真实世界数据集上进行了大量实验，证明了我们方法的优越性能。我们的代码已公开可用。
英文摘要：
Medical time series has been playing a vital role in real-world healthcare systems as valuable information in monitoring health conditions of patients. Accurate classification for medical time series, e.g., Electrocardiography (ECG) signals, can help for early detection and diagnosis. Traditional methods towards medical time series classification rely on handcrafted feature extraction and statistical methods; with the recent advancement of artificial intelligence, the machine learning and deep learning methods have become more popular. However, existing methods often fail to fully model the complex spatial dynamics under different scales, which ignore the dynamic multi-resolution spatial and temporal joint inter-dependencies. Moreover, they are less likely to consider the special baseline wander problem as well as the multi-view characteristics of medical time series, which largely hinders their prediction performance. To address these limitations, we propose a Multi-resolution Spatiotemporal Graph Learning framework, MedGNN, for medical time series classification. Specifically, we first propose to construct multi-resolution adaptive graph structures to learn dynamic multi-scale embeddings. Then, to address the baseline wander problem, we propose Difference Attention Networks to operate self-attention mechanisms on the finite difference for temporal modeling. Moreover, to learn the multi-view characteristics, we utilize the Frequency Convolution Networks to capture complementary information of medical time series from the frequency domain. In addition, we introduce the Multi-resolution Graph Transformer architecture to model the dynamic dependencies and fuse the information from different resolutions. Finally, we have conducted extensive experiments on multiple medical real-world datasets that demonstrate the superior performance of our method. Our Code is available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
