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Gait phenotypes in paediatric hereditary spastic paraplegia revealed by dynamic time warping analysis and random forests

机译:动态时间规整分析和随机森林揭示小儿遗传性痉挛性截瘫的步态表型

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摘要

The Hereditary Spastic Paraplegias (HSP) are a group of heterogeneous disorders with a wide spectrum of underlying neural pathology, and hence HSP patients express a variety of gait abnormalities. Classification of these phenotypes may help in monitoring disease progression and personalizing therapies. This is currently managed by measuring values of some kinematic and spatio-temporal parameters at certain moments during the gait cycle, either in the doctor´s surgery room or after very precise measurements produced by instrumental gait analysis (IGA). These methods, however, do not provide information about the whole structure of the gait cycle. Classification of the similarities among time series of IGA measured values of sagittal joint positions throughout the whole gait cycle can be achieved by hierarchical clustering analysis based on multivariate dynamic time warping (DTW). Random forests can estimate which are the most important isolated parameters to predict the classification revealed by DTW, since clinicians need to refer to them in their daily practice. We acquired time series of pelvic, hip, knee, ankle and forefoot sagittal angular positions from 26 HSP and 33 healthy children with an optokinetic IGA system. DTW revealed six gait patterns with different degrees of impairment of walking speed, cadence and gait cycle distribution and related with patient’s age, sex, GMFCS stage, concurrence of polyneuropathy and abnormal visual evoked potentials or corpus callosum. The most important parameters to differentiate patterns were mean pelvic tilt and hip flexion at initial contact. Longer time of support, decreased values of hip extension and increased knee flexion at initial contact can differentiate the mildest, near to normal HSP gait phenotype and the normal healthy one. Increased values of knee flexion at initial contact and delayed peak of knee flexion are important factors to distinguish GMFCS stages I from II-III and concurrence of polyneuropathy.
机译:遗传性痉挛性截瘫(HSP)是一组具有广泛的潜在神经病理学的异质性疾病,因此HSP患者表现出多种步态异常。这些表型的分类可能有助于监测疾病的进展和个性化治疗。目前,这是通过在步态周期的某些时刻测量某些运动学和时空参数的值来进行管理的,无论是在医生的手术室中还是在通过仪器步态分析(IGA)进行了非常精确的测量之后。但是,这些方法不能提供有关步态周期整个结构的信息。整个步态周期中矢状关节位置的IGA测量值的时间序列之间的相似性分类可以通过基于多元动态时间规整(DTW)的层次聚类分析来实现。随机森林可以估计哪些是预测DTW揭示的分类的最重要的孤立参数,因为临床医生在日常实践中需要参考它们。我们从26名HSP和33名健康儿童的视动IGA系统中获得了骨盆,髋,膝,踝和前脚矢状位的时间序列。 DTW揭示了六个步态模式,这些步态模式对步行速度,节奏和步态周期分布有不同程度的损害,并且与患者的年龄,性别,GMFCS分期,多发性神经病并发以及异常的视觉诱发电位或体相关。区分模式的最重要参数是初次接触时的平均骨盆倾斜度和髋屈曲度。更长的支持时间,初次接触时髋关节伸展值降低和膝盖屈曲增加可以区分最轻,接近正常的HSP步态表型和正常健康的步态表型。初次接触时膝关节屈曲值的增加和膝关节屈曲峰的延迟是区分GMFCS I期与II-III期以及多发神经病并发的重要因素。

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