Yue Yang, Chang Na, Shi Zonglin
Department of Mechanical Engineering, University College London, London, United Kingdom.
Department of Neurology, Huaihe Hospital, Henan University, Kaifeng, China.
Front Neurol. 2025 Jun 16;16:1607273. doi: 10.3389/fneur.2025.1607273. eCollection 2025.
Based on the asymmetric characteristics of left and right movements in patients with neurodegenerative diseases and their inherent coupling relationships, as well as the inevitable internal connection between them according to the principles of mechanical kinematics, and a processing method for the ratio of gait signals to left and right limb data is proposed. Using gait time series data collected from left and right limbs via pressure-sensitive insoles, a comparison was conducted among patients with Parkinson's disease (PD), Amyotrophic Lateral Sclerosis (ALS), Huntington's disease (HD), and a healthy control group (Ctrl) in terms of the average, standard deviation, and coefficient of variation of the left and right sequences, as well as the ratios between them. It was discovered that there exists a close correlation between the ratios of left to right sequences and the actual standard deviation and coefficient of variation of these sequences. These ratios can be utilized for identifying the categories of PD, ALS, and HD patients. After using a median filter ( = 3) to filter four sets of stride ratio data (Ctr1, A1s, PD, and HD), it was found that the data before filtering generally showed significant fluctuations, with many peaks and valleys, indicating that the original data may contain a lot of noise or outliers. In contrast, the filtered data showed relatively smaller fluctuations and a smoother curve, indicating that the filtering process effectively reduced noise in the data and enhanced its stability. The raw data distribution for the left and right limbs of patients with PD, ALS, HD, and the Ctrl was relatively large, posing certain difficulties in analyzing the patients' diseases. The use of the ratio of left to right data effectively improves the discreteness of the data. The ranking of CO complexity features from highest to lowest is ALS, HD, PD, and Ctrl. The ranking of sample entropy features from largest to smallest is ALS, HD, PD, and Ctrl. The ranking of wavelet coefficient features from largest to smallest is ALS, PD, HD, and Ctrl.
基于神经退行性疾病患者左右运动的不对称特征及其内在耦合关系，以及根据机械运动学原理它们之间不可避免的内在联系，提出了一种步态信号与左右肢体数据比值的处理方法。利用通过压敏鞋垫从左右肢体收集的步态时间序列数据，对帕金森病（PD）、肌萎缩侧索硬化症（ALS）、亨廷顿舞蹈病（HD）患者以及健康对照组（Ctrl）在左右序列的平均值、标准差和变异系数以及它们之间的比值方面进行了比较。发现左右序列的比值与这些序列的实际标准差和变异系数之间存在密切相关性。这些比值可用于识别PD、ALS和HD患者的类别。在使用中值滤波器（ = 3）对四组步幅比值数据（Ctr1、A1s、PD和HD）进行滤波后，发现滤波前的数据通常显示出明显的波动，有许多峰谷，表明原始数据可能包含大量噪声或异常值。相比之下，滤波后的数据波动相对较小，曲线更平滑，表明滤波过程有效地降低了数据中的噪声并增强了其稳定性。PD、ALS、HD患者以及Ctrl左右肢体的原始数据分布相对较大，给分析患者疾病带来一定困难。使用左右数据的比值有效地提高了数据的离散性。CO复杂度特征从高到低的排序为ALS、HD、PD、Ctrl。样本熵特征从大到小的排序为ALS、HD、PD、Ctrl。小波系数特征从大到小的排序为ALS、PD、HD、Ctrl。
