Detrended fluctuation analysis and adaptive fractal analysis of stride time data in Parkinson's disease: stitching together short gait trials.
Detrended fluctuation analysis and adaptive fractal analysis of stride time data in Parkinson's disease: stitching together short gait trials.
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DOI：
10.1371/journal.pone.0085787
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Haas CT
中科院分区：
文献类型：
--
作者：
Kirchner M;Schubert P;Liebherr M;Haas CT
变异性表明运动控制障碍，适合于识别步态病理。它可以通过线性参数（幅度估计）和更复杂的非线性方法（结构信息）来量化。去趋势波动分析（DFA）是一种测量结构信息的方法，例如，stride时间序列最近，一种改进的方法，自适应分形分析（AFA），已被提出。该方法以前没有应用于步态数据。分形缩放方法（FS）需要长的步幅到步幅的数据，以获得有效的结果。然而，在临床研究中，通常不测量大量步幅（例如， 步幅）。其中，由于人行道短，临床步态分析受到限制，因此，FS似乎不适用。本研究的目的是在临床条件下评估FS。测量了PD受试者和健康对照组（CG）的五次自定步速步行试验（每次步幅）的步幅时间数据。为了生成更长的时间序列，将步幅时间序列缝合在一起。计算变异系数（CV）、分形标度指数（DFA）和（AFA）。进行了两个替代检验：A）整个时间序列随机混洗; B）单个试验单独随机混洗，然后缝合在一起。CV在PD和CG之间没有区别。然而，PD和CG之间的显着差异被发现有关和。替代版本B产生的均方误差和经验分位数高于版本A。因此，我们得出结论，拼接过程创建了一个人工结构，导致高估了真实。将步态的部分拼接在一起的方法似乎是适当的，以区分PD和CG与FS。它提供了一种方法，将FS作为标准集成在临床步态分析中，并克服了人行道短等限制。
Variability indicates motor control disturbances and is suitable to identify gait pathologies. It can be quantified by linear parameters (amplitude estimators) and more sophisticated nonlinear methods (structural information). Detrended Fluctuation Analysis (DFA) is one method to measure structural information, e.g., from stride time series. Recently, an improved method, Adaptive Fractal Analysis (AFA), has been proposed. This method has not been applied to gait data before. Fractal scaling methods (FS) require long stride-to-stride data to obtain valid results. However, in clinical studies, it is not usual to measure a large number of strides (e.g., strides). Amongst others, clinical gait analysis is limited due to short walkways, thus, FS seem to be inapplicable. The purpose of the present study was to evaluate FS under clinical conditions. Stride time data of five self-paced walking trials ( strides each) of subjects with PD and a healthy control group (CG) was measured. To generate longer time series, stride time sequences were stitched together. The coefficient of variation (CV), fractal scaling exponents (DFA) and (AFA) were calculated. Two surrogate tests were performed: A) the whole time series was randomly shuffled; B) the single trials were randomly shuffled separately and afterwards stitched together. CV did not discriminate between PD and CG. However, significant differences between PD and CG were found concerning and . Surrogate version B yielded a higher mean squared error and empirical quantiles than version A. Hence, we conclude that the stitching procedure creates an artificial structure resulting in an overestimation of true . The method of stitching together sections of gait seems to be appropriate in order to distinguish between PD and CG with FS. It provides an approach to integrate FS as standard in clinical gait analysis and to overcome limitations such as short walkways.
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