基于VGRF信号的帕金森病检测步态分析:多尺度有向图神经网络方法。

IF 6.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Xiaotian Wang, Xuanhang Xu, Zhifu Zhao, Fu Li, Fei Qi, Shuo Liang
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引用次数: 0

摘要

帕金森病(PD)通常以异常的步态模式为特征,可以使用垂直地面反作用力(VGRF)信号客观定量地诊断。先前的研究已经证明了深度学习在VGRF信号分析中的有效性。然而,没有充分考虑VGRF信号固有的图结构,限制了动态步态特征的表示。为了解决这个问题,我们提出了一种多尺度自适应有向图神经网络(MS-ADGNN)方法来区分帕金森患者和健康对照者的步态。该方法将VGRF信号建模为多尺度有向图,捕捉足底传感器内部的分布关系和行走过程中的动态压力传导。MS-ADGNN集成了自适应有向图网络(ADGN)单元和多尺度时间卷积网络(MSTCN)单元。ADGN从有向图的三个尺度提取空间特征,有效捕获局部和全局连通性。MSTCN提取多尺度时间特征,捕获短期到长期的依赖关系。该方法在三个广泛使用的数据集上优于现有方法。在跨数据集实验中,准确率、f1分数和几何平均值的平均提高分别为2.46美元、1.25美元和1.11美元。同时,在10倍交叉验证实验中,改进率分别为0.78美元、0.83美元和0.81美元。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
VGRF Signal-Based Gait Analysis for Parkinson's Disease Detection: A Multi-Scale Directed Graph Neural Network Approach.

Parkinson's Disease (PD) is often characterized by abnormal gait patterns, which can be objectively and quantitatively diagnosed using Vertical Ground Reaction Force (VGRF) signals. Previous studies have demonstrated the effectiveness of deep learning in VGRF signal analysis. However, the inherent graph structure of VGRF signals has not been adequately considered, limiting the representation of dynamic gait characteristics. To address this, we propose a Multi-Scale Adaptive Directed Graph Neural Network (MS-ADGNN) approach to distinguish the gaits between Parkinson's patients and healthy controls. This method models the VGRF signal as a multi-scale directed graph, capturing the distribution relationships within the plantar sensors and the dynamic pressure conduction during walking. MS-ADGNN integrates an Adaptive Directed Graph Network (ADGN) unit and a Multi-Scale Temporal Convolutional Network (MSTCN) unit. ADGN extracts spatial features from three scales of the directed graph, effectively capturing local and global connectivity. MSTCN extracts multi-scale temporal features, capturing short to long-term dependencies. The proposed method outperforms existing methods on three widely used datasets. In cross-dataset experiments, the average improvements in terms of accuracy, F1-score, and geometric mean are 2.46$\%$, 1.25$\%$, and 1.11$\%$ respectively. Meanwhile, in 10-fold cross-validation experiments, the improvements are 0.78$\%$, 0.83$\%$, and 0.81$\%$ respectively.

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来源期刊
IEEE Journal of Biomedical and Health Informatics
IEEE Journal of Biomedical and Health Informatics COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
CiteScore
13.60
自引率
6.50%
发文量
1151
期刊介绍: IEEE Journal of Biomedical and Health Informatics publishes original papers presenting recent advances where information and communication technologies intersect with health, healthcare, life sciences, and biomedicine. Topics include acquisition, transmission, storage, retrieval, management, and analysis of biomedical and health information. The journal covers applications of information technologies in healthcare, patient monitoring, preventive care, early disease diagnosis, therapy discovery, and personalized treatment protocols. It explores electronic medical and health records, clinical information systems, decision support systems, medical and biological imaging informatics, wearable systems, body area/sensor networks, and more. Integration-related topics like interoperability, evidence-based medicine, and secure patient data are also addressed.
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