一种用于帕金森病检测的全局-局部动态有向图神经网络。

IF 5.2 2区 医学 Q2 ENGINEERING, BIOMEDICAL
Xiaotian Wang, Guanhai Zhou, Zhifu Zhao, Xiaoyi Zhang, Fu Li, Fei Qi
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引用次数: 0

摘要

利用垂直地面反作用力(VGRF)信号进行步态分析的图神经网络(GNNs)在帕金森病(PD)诊断和康复领域显示出巨大的潜力。然而,该领域现有的基于gnn的方法通常将VGRF信号建模为静态拓扑结构,而忽略了VGRF图结构在行走过程中的动态变化。为了解决这一问题,提出了一种全局-局部动态有向图神经网络(GLD2-GNN)来表征VGRF信号的动态时空特征。该模型的核心组件是DyDGNN块,该块由动态图学习(DGL)单元、动态有向图网络(DyDGN)单元和时间卷积网络(TCN)单元组成。首先,提出DGL单元来学习VGRF信号的动态拓扑关系。基于学习到的图结构,构建DyDGN单元,从VGRF信号中提取空间模式和捕获拓扑动态特征。然后,通过TCN单元提取VGRF信号的局部时间模式。在Ga、Ju和Si三个数据集上通过k-fold交叉验证和跨数据集验证对该方法进行了评估。与RFdGAD、Transformer和AST-DGNN等现有方法相比,GLD2-GNN在验证实验中表现出更优越的性能。值得注意的是,我们的方法在跨数据集验证中实现了4.45%的准确率,2.93%的F1分数和2.88%的几何平均值的平均提高。大量实验表明,通过捕获VGRF信号的拓扑结构动态和时空特征,GLD2-GNN具有对复杂步态模式的表征能力和跨不同数据集的泛化能力。对于未来的工作,我们计划将我们的方法与多模态方法相结合,并将我们的框架集成到一个完整的步态分析系统中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Global-Local Dynamic Directed Graph Neural Network for Parkinson's Disease Detection.

Graph Neural Networks (GNNs) for gait analysis utilizing Vertical Ground Reaction Force (VGRF) signals have demonstrated significant potential in Parkinson's Disease (PD) diagnosis and rehabilitation fields. However, existing GNN-based methods in this area normally model the VGRF signals as static topological structures, and ignore the dynamic variations of the VGRF graph structures during walking. To address this issue, a Global-Local Dynamic Directed Graph Neural Network (GLD2-GNN) is proposed to represent the dynamic spatio-temporal features of VGRF signals. The core component of the proposed model is the DyDGNN block, which is composed of a Dynamic Graph Learning (DGL) unit, a Dynamic Directed Graph Network (DyDGN) unit, and a Temporal Convolutional Network (TCN) unit. First, the DGL unit is proposed to learn dynamic topological relationships of VGRF signals. Based on learned graph structures, the DyDGN unit is constructed to extract the spatial patterns and capture topological dynamic features from VGRF signals. Subsequently, local temporal patterns of VGRF signals are extracted by the TCN unit. The proposed method is evaluated through k-fold cross-validation and cross-dataset validation on three datasets Ga, Ju and Si. Compared with existing methods, such as RFdGAD, Transformer, and AST-DGNN, GLD2-GNN demonstrates superior performance in the validation experiments. Notably, our method achieves an average improvement of 4.45% in accuracy, 2.93% in F1 score, and 2.88% in geometric mean across cross-dataset validation. Extensive experiments have demonstrated that GLD2-GNN exhibits both the representational ability for complex gait patterns and the generalization ability across various datasets by capturing dynamics of VGRF topological structures and spatio-temporal features from VGRF signals. For future work, we plan to combine our method with multi-modal methods and integrate our framework into a complete gait analysis system.

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来源期刊
CiteScore
8.60
自引率
8.20%
发文量
479
审稿时长
6-12 weeks
期刊介绍: Rehabilitative and neural aspects of biomedical engineering, including functional electrical stimulation, acoustic dynamics, human performance measurement and analysis, nerve stimulation, electromyography, motor control and stimulation; and hardware and software applications for rehabilitation engineering and assistive devices.
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