Department of Medical and Surgical Sciences and Biotechnologies, "Sapienza" University of Rome, Polo Pontino, 04100 Latina, Italy.
Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, 00078 Monte Porzio Catone, Italy.
Sensors (Basel). 2023 May 22;23(10):4983. doi: 10.3390/s23104983.
The aim of this study was to assess the ability of multiscale sample entropy (MSE), refined composite multiscale entropy (RCMSE), and complexity index (CI) to characterize gait complexity through trunk acceleration patterns in subjects with Parkinson's disease (swPD) and healthy subjects, regardless of age or gait speed. The trunk acceleration patterns of 51 swPD and 50 healthy subjects (HS) were acquired using a lumbar-mounted magneto-inertial measurement unit during their walking. MSE, RCMSE, and CI were calculated on 2000 data points, using scale factors () 1-6. Differences between swPD and HS were calculated at each , and the area under the receiver operating characteristics, optimal cutoff points, post-test probabilities, and diagnostic odds ratios were calculated. MSE, RCMSE, and CIs showed to differentiate swPD from HS. MSE in the anteroposterior direction at 4 and 5, and MSE in the ML direction at 4 showed to characterize the gait disorders of swPD with the best trade-off between positive and negative posttest probabilities and correlated with the motor disability, pelvic kinematics, and stance phase. Using a time series of 2000 data points, a scale factor of 4 or 5 in the MSE procedure can yield the best trade-off in terms of post-test probabilities when compared to other scale factors for detecting gait variability and complexity in swPD.
本研究旨在评估多尺度样本熵（MSE）、精细化复合多尺度熵（RCMSE）和复杂度指数（CI）在不考虑年龄或步速的情况下，通过对帕金森病患者（swPD）和健康受试者（HS）的躯干加速度模式，用于表征步态复杂度的能力。使用腰椎安装的磁惯性测量单元，在受试者行走过程中，采集了 51 名 swPD 和 50 名健康受试者（HS）的躯干加速度模式。在 2000 个数据点上计算了 MSE、RCMSE 和 CI，使用了尺度因子（）1-6。在每个尺度因子下计算了 swPD 和 HS 之间的差异，并计算了接受者操作特征曲线下的面积、最佳截断点、后验概率和诊断比值比。MSE、RCMSE 和 CIs 显示可区分 swPD 和 HS。在前后方向上的 MSE 在 4 和 5，以及在 ML 方向上的 MSE 在 4 显示出可最佳描述 swPD 的步态障碍，在阳性和阴性后验概率之间具有最佳的折衷，并且与运动障碍、骨盆运动学和站立阶段相关。使用 2000 个数据点的时间序列，与其他尺度因子相比，在 MSE 过程中使用 4 或 5 的尺度因子可以在检测 swPD 中的步态变异性和复杂性方面获得最佳的后验概率折衷。
