Objective: Gait may be a useful biomarker that can be objectively measured with wearable technology to classify Parkinson's disease (PD). This study aims to: (i) comprehensively quantify a battery of commonly utilized gait digital characteristics (spatiotemporal and signal-based), and (ii) identify the best discriminative characteristics for the optimal classification of PD. Methods: Six partial least square discriminant analysis (PLS-DA) models were trained on subsets of 210 characteristics measured in 142 subjects (81 people with PD, 61 controls (CL)). Results: Models accuracy ranged between 70.42-88.73% (AUC: 78.4-94.5%) with a sensitivity of 72.84-90.12% and a specificity of 60.3-86.89%. Signal-based digital gait characteristics independently gave 87.32% accuracy. The most influential characteristics in the classification models were related to root mean square values, power spectral density, step velocity and length, gait regularity and age. Conclusions: This study highlights the importance of signal-based gait characteristics in the development of tools to help classify PD in the early stages of the disease.

中文翻译：

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用于帕金森病分类的基于加速度计的数字步态特征：重要的是什么？

目的：步态可能是一种有用的生物标志物，可以通过可穿戴技术客观地测量，从而对帕金森病 (PD) 进行分类。本研究旨在：(i) 全面量化一组常用的步态数字特征（基于时空和信号），以及 (ii) 确定 PD 最佳分类的最佳判别特征。方法：6 个偏最小二乘判别分析 (PLS-DA) 模型对 142 名受试者（81 名 PD 患者，61 名对照 (CL)）中测量的 210 个特征的子集进行了训练。结果：模型准确度介于 70.42-88.73%（AUC：78.4-94.5%）之间，敏感性为 72.84-90.12%，特异性为 60.3-86.89%。基于信号的数字步态特征独立给出了 87.32% 的准确度。分类模型中影响最大的特征与均方根值、功率谱密度、步速和步长、步态规律和年龄有关。结论：本研究强调了基于信号的步态特征在开发工具以帮助对疾病早期阶段的 PD 进行分类中的重要性。