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首页> 外文会议>International Conference on Computational Science and Its Applications >Gait Characteristics and Their Discriminative Ability in Patients with Fabry Disease with and Without White-Matter Lesions
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Gait Characteristics and Their Discriminative Ability in Patients with Fabry Disease with and Without White-Matter Lesions

机译：用白色物质病变的法布里疾病患者的步态特征及其鉴别能力

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Fabry disease (FD) is a rare disease commonly complicated with white matter lesions (WMLs). WMLs, which have extensively been associated with gait impairment, justify further investigation of its implication in FD. This study aims to identify a set of gait characteristics to discriminate FD patients with/without WMLs and healthy controls. Seventy-six subjects walked through a predefined circuit using gait sensors that continuously acquired different stride features. Data were normalized using multiple regression normalization taking into account the subject physical properties, with the assessment of 32 kinematic gait variables. A filter method (Mann Whitney U test and Pearson correlation) followed by a wrapper method (recursive feature elimination (RFE) for Logistic Regression (LR) and Support Vector Machine (SVM) and information gain for Random Forest (RF)) were used for feature selection. Then, five different classifiers (LR, SVM Linear and RBF kernel, RF, and K-Nearest Neighbors (KNN)) based on different selected set features were evaluated. For FD patients with WMLs versus controls the highest accuracy of 72% was obtained using LR based on 3 gait variables: pushing, foot flat, and maximum toe clearance 2. For FD patients without WMLs versus controls, the best performance was observed using LR and SVM RBF kernel based on loading, foot flat, minimum toe clearance, stride length variability, loading variability, and lift-off angle variability with an accuracy of 83%. These findings are the first step to demonstrate the potential of machine learning techniques based on gait variables as a complementary tool to understand the role of WMLs in the gait impairment of FD.
机译：法布里病（FD）是一种罕见的疾病，通常与白质病变（WML）复杂化。 WMLS广泛地与步态减值有关，证明了进一步调查其在FD中的含义。本研究旨在识别一组步态特征，以区分FD患者/不含WML和健康对照。使用连续获取不同步幅特性的步态传感器，通过预定义电路走路，七十六个受试者走路。使用多元回归归一化进行标准化，以考虑到主题物理属性，评估32个运动步态变量。使用过滤方法（Mann Whitney U测试和Pearson相关性），然后是包装方法（用于逻辑回归（LR）和支持向量机（SVM）和随机森林（RF）的信息增益）的包装方法（RFE）和支持向量机（RF））（RF））功能选择。然后，评估了基于不同选定的集合特征的五种不同的分类器（LR，SVM线性和RBF内核，RF和K最近邻居（KNN））。对于WMLS与控制的FD患者使用LR基于3步态变量获得的最高精度为72％：推动，脚平和最大趾孔清除。对于没有WML与对照的FD患者，使用LR和LR的最佳性能SVM RBF内核基于负载，脚平，最小脚趾间隙，步幅长度可变性，装载可变性和剥离角度变异，精度为83％。这些发现是基于步态变量作为互补工具来展示机器学习技术的潜力的第一步，以了解WML在步态损害FD的过程中的作用。

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