Autoregressive modeling to assess stride time pattern stability in individuals with Huntington's disease
Autoregressive modeling to assess stride time pattern stability in individuals with Huntington's disease
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DOI：
10.1186/s12883-019-1545-6
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发表时间：
2019-12-09
期刊：
影响因子：
2.6
通讯作者：
Morgan, Kristin D.
中科院分区：
文献类型：
--
作者：
Alzakerin, Helia Mahzoun;Halkiadakis, Yannis;Morgan, Kristin D.
背景：亨廷顿病(HD)是一种进行性神经系统疾病，可导致认知和身体损害。这些损害会影响个人的步态，随着疾病的发展，它会显著改变一个人的稳定性。先前的研究发现，改变步态的时间模式有助于区分健康步态和病态步态。自回归(AR)建模是一种对数据中潜在的时间模式进行建模的统计技术。在这里，AR模型评估了对照组和HD患者在步态步态时间模式稳定性方面的差异。基于AR模型系数及其在平稳三角形上的位置来确定步幅时间模式稳定性的差异，该平稳三角形提供了模式的均值、方差和自相关随时间变化的直观表示。因此，表现出相似步幅时间模式稳定性的个体将居住在平稳性三角形的同一区域。根据AR模型系数及其在平稳三角中的位置，假设HD患者的步态稳定性比对照组有更大的变化。方法：16名对照组和20名HD患者执行5分钟步行方案。从协议中提取的连续步长时间构造时间序列，并对步长时间序列数据进行二阶AR模型拟合。结果：HD患者的AR模型系数(AR1p<；0.001；AR2p<；0.001)显著改变了HD患者的跨步时间模式稳定性。结论：成功地描述了HD患者和对照组之间的AR系数。HD患者居住在更靠近静止三角的振荡区域内，这可能反映了在该人群中普遍观察到的振荡神经元活动。能够定量和直观地检测步态时间行为的差异，突出了这种方法在识别HD患者步态损害方面的潜力。
Background: Huntington's disease (HD) is a progressive, neurological disorder that results in both cognitive and physical impairments. These impairments affect an individual's gait and, as the disease progresses, it significantly alters one's stability. Previous research found that changes in stride time patterns can help delineate between healthy and pathological gait. Autoregressive (AR) modeling is a statistical technique that models the underlying temporal patterns in data. Here the AR models assessed differences in gait stride time pattern stability between the controls and individuals with HD. Differences in stride time pattern stability were determined based on the AR model coefficients and their placement on a stationarity triangle that provides a visual representation of how the patterns mean, variance and autocorrelation change with time. Thus, individuals who exhibit similar stride time pattern stability will reside in the same region of the stationarity triangle. It was hypothesized that individuals with HD would exhibit a more altered stride time pattern stability than the controls based on the AR model coefficients and their location in the stationarity triangle.Methods: Sixteen control and twenty individuals with HD performed a five-minute walking protocol. Time series' were constructed from consecutive stride times extracted during the protocol and a second order AR model was fit to the stride time series data. A two-sample t-test was performed on the stride time pattern data to identify differences between the control and HD groups.Results: The individuals with HD exhibited significantly altered stride time pattern stability than the controls based on their AR model coefficients (AR1 p < 0.001; AR2 p < 0.001).Conclusions: The AR coefficients successfully delineated between the controls and individuals with HD. Individuals with HD resided closer to and within the oscillatory region of the stationarity triangle, which could be reflective of the oscillatory neuronal activity commonly observed in this population. The ability to quantitatively and visually detect differences in stride time behavior highlights the potential of this approach for identifying gait impairment in individuals with HD.