Spiral analysis - Improved clinical utility with center detection
Spiral analysis - Improved clinical utility with center detection
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DOI：
10.1016/j.jneumeth.2008.03.009
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发表时间：
2008-06-30
影响因子：
3
通讯作者：
Pullman, Seth L.
中科院分区：
文献类型：
--
作者：
Wang, Hongzhi;Yu, Qiping;Pullman, Seth L.
螺旋分析是一种计算机化方法，通过手写的阿基米德螺旋来测量人类运动表现。它量化正常的运动活动，并检测早期疾病以及运动障碍患者的功能障碍。螺旋分析的临床应用是基于从原始螺旋轨迹导出的运动学和动力学指标，必须以高精度检测这些指标并将其转化为数学表达式。准确确定螺旋的中心并减少由中心选择错误引起的寄生低频噪声对于分析非常重要。手写螺旋即使在纸上标记，也不会全部从同一点开始，并且绘图伪影不易过滤，不会导致螺旋数据失真和性能指标损坏。在本文中，我们描述了一种用于检测最佳螺旋中心并减少不需要的绘图报告、伪影的方法。为了证明螺旋分析的整体改进，我们分别研究了不同频域中最佳螺旋中心检测的影响，发现它显着提高了低频域中的临床螺旋测量精度。 (c) 2008 年爱思唯尔 BY。版权所有
Spiral analysis is a computerized method that measures human motor performance from handwritten Archimedean spirals. It quantifies normal motor activity, and detects early disease as well as dysfunction in patients with movement disorders. The clinical utility of spiral analysis is based on kinematic and dynamic indices derived from the original spiral trace, which must be detected and transformed into mathematical expressions with great precision. Accurately determining the center of the spiral and reducing spurious low frequency noise caused by center selection error is important to the analysis.Handwritten spirals do not all start at the same point, even when marked on paper, and drawing artifacts are not easily filtered without distortion of the spiral data and corruption of the performance indices. In this we describe a method for detecting the optimal spiral center and reducing the unwanted drawing report, artifacts. To demonstrate overall improvement to spiral analysis, we study the impact of the optimal spiral center detection in different frequency domains separately and find that it notably improves the clinical spiral measurement accuracy in low frequency domains. (c) 2008 Elsevier BY. All rights reserved