Deffeyes Joan E, Kochi Naomi, Harbourne Regina T, Kyvelidou Anastasia, Stuberg Wayne A, Stergiou Nicholas
University of Nebraska, Omaha, Nebraska 68182-0216, USA.
Nonlinear Dynamics Psychol Life Sci. 2009 Oct;13(4):351-68.
Upright sitting is one of the first motor skills an infant learns, and thus sitting postural control provides an early window into the infant's motor development. Early identification of infants with motor developmental delay, such as infants with cerebral palsy, allows for early therapeutic intervention by physical therapists. Early intervention is thought to produce better outcomes, due to greater neural plasticity in younger infants. Postural sway, as measured by a force plate, can be used to objectively and quantitatively characterize infant motor control during sitting. Pathology, such as cerebral palsy, may alter the fractal properties of motor function. Often physiologic time series data, including infant sitting postural sway data, is mathematically non-stationary. Detrended Fluctuation Analysis (DFA) is useful to characterize the fractal nature of time series data because it is does not assume stationarity of the data. In this study we found that suitable selection of the order of the detrending function improves the performance of the DFA algorithm, with a higher order polynomial detrending better able to distinguish infant sitting posture time series data from Brown noise (random walk), and first order detrending better able to distinguish infants with motor delay (cerebral palsy) from infants with typical development.
直立坐姿是婴儿最早学会的运动技能之一，因此坐姿姿势控制为了解婴儿的运动发育提供了一个早期窗口。早期识别运动发育迟缓的婴儿，如脑瘫婴儿，可使物理治疗师进行早期治疗干预。由于年幼儿童具有更大的神经可塑性，早期干预被认为能产生更好的效果。通过测力板测量的姿势摆动可用于客观、定量地表征婴儿坐姿时的运动控制。诸如脑瘫等病理情况可能会改变运动功能的分形特性。通常，包括婴儿坐姿姿势摆动数据在内的生理时间序列数据在数学上是非平稳的。去趋势波动分析（DFA）有助于表征时间序列数据的分形性质，因为它不假定数据具有平稳性。在本研究中，我们发现去趋势函数阶数的合适选择可提高DFA算法的性能，高阶多项式去趋势能更好地将婴儿坐姿时间序列数据与布朗噪声（随机游走）区分开来，而一阶去趋势能更好地将运动发育迟缓（脑瘫）婴儿与发育正常的婴儿区分开来。
