Vector-field statistics for the analysis of time varying clinical gait data
Vector-field statistics for the analysis of time varying clinical gait data
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DOI：
10.1016/j.clinbiomech.2016.11.008
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发表时间：
2017-01-01
影响因子：
1.8
通讯作者：
Robinson, M. A.
中科院分区：
文献类型：
--
作者：
Donnelly, C. J.;Alexander, C.;Robinson, M. A.
背景资料：在临床环境中，步态数据的时变分析严重依赖于评估这些生物信号的个体的经验。虽然三维运动学被认为是时变波形（1D），但这些数据的探索性统计分析通常使用多个离散或OD因变量进行。在没有先验OD假设的情况下，临床医生在分析时变步态特征时存在I型和II型错误的风险，因为事件统计与首选的主观临床评估方法结合使用。本通信的目的是确定矢量场波形统计是否能够提供定量确证的实际显着差异随时间变化的步态signatures确定由两个临床训练的gait experts.Methods：该案例研究是左偏瘫脑瘫（GMFCS I）步态患者肉毒杆菌毒素（BoNT-A）注射到他们的左腓肠肌。当比较两个测试者之间的主观临床步态评估时，他们在61%的关节自由度和分析的运动相位方面彼此一致。对于测试仪1和测试仪2，它们与78%和53%的运动学变量分析的矢量场分析一致。当测试者1和测试者2的主观分析汇总在一起，然后比较的向量场分析，他们在协议的83%的随时间变化的运动学variables analysed.Interpretation：这些结果表明，在原则上，向量场的统计数据证实了什么临床步态专家团队将归类为实际有意义的前后随时间变化的运动学差异。潜在的向量场统计被用作一个有用的临床工具，客观分析随时间变化的临床步态数据。建议未来的研究，以评估向量场分析在临床决策过程中的有用性。(C)2016爱思唯尔有限公司版权所有
Background: In clinical settings, the time varying analysis of gait data relies heavily on the experience of the individual(s) assessing these biological signals. Though three dimensional kinematics are recognised as time varying waveforms(1D), exploratory statistical analysis of these data are commonly carried out with multiple discrete or OD dependent variables. In the absence of an a priori OD hypothesis, clinicians are at risk of making type I and II errors in their analyis of time varying gait signatures in the event statistics are used in concert with prefered subjective clinical assesment methods. The aim of this communication was to determine if vector field waveform statistics were capable of providing quantitative corroboration to practically significant differences in time varying gait signatures as determined by two clinically trained gait experts.Methods: The case study was a left hemiplegic Cerebral Palsy (GMFCS I) gait patient following a botulinum toxin (BoNT-A) injection to their left gastrocnemius muscle.Findings: When comparing subjective clinical gait assessments between two testers, they were in agreement with each other for 61% of the joint degrees of freedom and phases of motion analysed. For tester 1 and tester 2, they were in agreement with the vector-field analysis for 78% and 53% of the kinematic variables analysed. When the subjective analyses of tester 1 and tester 2 were pooled together and then compared to the vector field analysis, they were in agreement for 83% of the time varying kinematic variables analysed.Interpretation: These outcomes demonstrate that in principle, vector-field statistics corroborates with what a team of clinical gait experts would classify as practically meaningful pre-versus post time varying kinematic differences. The potential for vector-field statistics to be used as a useful clinical tool for the objective analysis of time varying clinical gait data is established. Future research is recommended to assess the usefulness of vector-field analyses during the clinical decision making process. (C) 2016 Elsevier Ltd. All rights reserved.