Robinson Mark A, Vanrenterghem Jos, Pataky Todd C
Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, UK.
Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, UK.
J Electromyogr Kinesiol. 2015 Feb;25(1):14-9. doi: 10.1016/j.jelekin.2014.10.018. Epub 2014 Nov 7.
Multi-muscle EMG time-series are highly correlated and time dependent yet traditional statistical analysis of scalars from an EMG time-series fails to account for such dependencies. This paper promotes the use of SPM vector-field analysis for the generalised analysis of EMG time-series. We reanalysed a publicly available dataset of Young versus Adult EMG gait data to contrast scalar and SPM vector-field analysis. Independent scalar analyses of EMG data between 35% and 45% stance phase showed no statistical differences between the Young and Adult groups. SPM vector-field analysis did however identify statistical differences within this time period. As scalar analysis failed to consider the multi-muscle and time dependence of the EMG time-series it exhibited Type II error. SPM vector-field analysis on the other hand accounts for both dependencies whilst tightly controlling for Type I and Type II error making it highly applicable to EMG data analysis. Additionally SPM vector-field analysis is generalizable to linear and non-linear parametric and non-parametric statistical models, allowing its use under constraints that are common to electromyography and kinesiology.
多肌肉肌电图时间序列具有高度相关性且依赖于时间,但对肌电图时间序列标量进行的传统统计分析未能考虑到这种依赖性。本文提倡使用统计参数映射(SPM)向量场分析对肌电图时间序列进行广义分析。我们重新分析了一个公开可用的数据集,该数据集包含青年与成人肌电图步态数据,以对比标量分析和SPM向量场分析。对站立期35%至45%之间的肌电图数据进行独立标量分析显示,青年组和成人组之间无统计学差异。然而,SPM向量场分析在此时间段内确实发现了统计学差异。由于标量分析未考虑肌电图时间序列的多肌肉和时间依赖性,它出现了II型错误。另一方面,SPM向量场分析考虑了这两种依赖性,同时严格控制I型和II型错误,使其非常适用于肌电图数据分析。此外,SPM向量场分析可推广到线性和非线性参数及非参数统计模型,允许在肌电图和运动机能学常见的约束条件下使用。
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