使用坐-立-坐运动的四阶段模型发现健康膝关节和骨关节炎膝关节之间声发射的差异。

Lik-Kwan Shark, Hongzhi Chen, John Goodacre
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引用次数: 52

摘要

通过对膝关节进行反复的坐-立-坐运动以产生应力,从健康膝关节和骨关节炎(OA)膝关节两个年龄匹配的组中获得膝关节释放的高频声发射(AE)的短暂瞬发,并从进行的信号分析中发现两组之间存在显著差异。分析是基于坐-立-坐运动的四阶段模型和声发射爆发的双特征描述符。这四个阶段是由运动过程中的关节角度测量得出的,它们分别是坐姿到站立运动中的上升-加速和上升-减速阶段,其次是站立到站立运动中的下降-加速和下降-减速阶段。这两个特征是从运动过程中的声发射测量中提取出来的,它们由每次声发射爆发的峰值幅度值和平均信号电平组成。结果表明,所提出的分析方法对年龄匹配的健康组和OA组的区分具有较高的灵敏度,其中最显著的差异来自于上升-减速阶段的峰值幅度,由于OA膝关节的AE爆发更多,峰值幅度更高,平均信号水平更高,因此基于图像的视觉显示其AE特征轮廓的数量和强度差异明显。在主成分形成的空间中有两个分离良好的簇。这些结果为进一步发展声发射作为一种新的工具来促进临床和家庭环境中膝关节的动态完整性评估提供了充分的支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Discovering differences in acoustic emission between healthy and osteoarthritic knees using a four-phase model of sit-stand-sit movements.

By performing repeated sit-stand-sit movements to create stress on knee joints, short transient bursts of high frequency acoustic emission (AE) released by the knee joints were acquired from two age matched groups consisting of healthy and osteoarthritic (OA) knees, and significant differences between these two groups were discovered from the signal analysis performed. The analysis is based on a four-phase model of sit-stand-sit movements and a two-feature descriptor of AE bursts. The four phases are derived from joint angle measurement during movement, and they consist of the ascending-acceleration and ascending-deceleration phases in the sit-to-stand movement, followed by the descending-acceleration and descending-deceleration phases in the stand-to-sit movement. The two features are extracted from AE measurement during movement, and they consist of the peak magnitude value and average signal level of each AE burst. The proposed analysis method is shown to provide a high sensitivity for differentiation of the two age matched healthy and OA groups, with the most significant difference found to come from the peak magnitude value in the ascending-deceleration phase, clear quantity and strength differences in the image based visual display of their AE feature profiles due to substantially more AE bursts from OA knee joints with higher peak magnitude values and higher average signal levels, and two well separated clusters in the space formed by the principal components. These results provide ample support for further development of AE as a novel tool to facilitate dynamic integrity assessment of knee joints in clinic and home settings.

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