Shark Lik-Kwan, Chen Hongzhi, Goodacre John
ADSIP (Applied Digital Signal and Image Processing) Research Centre, University of Central Lancashire, Preston, PR1 2HE, UK.
Open Med Inform J. 2010 Jul 27;4:116-25. doi: 10.2174/1874431101004010116.
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.
通过进行反复的坐立-坐立动作以对膝关节施加压力，从由健康膝关节和骨关节炎（OA）膝关节组成的两个年龄匹配组中获取膝关节释放的高频声发射（AE）的短瞬态爆发，并通过所进行的信号分析发现这两组之间存在显著差异。该分析基于坐立-坐立动作的四阶段模型和AE爆发的双特征描述符。这四个阶段源自运动过程中的关节角度测量，包括从坐到站运动中的上升加速和上升减速阶段，随后是从站到坐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AE测量中提取的，包括每个AE爆发的峰值大小值和平均信号水平。结果表明，所提出的分析方法对区分两个年龄匹配的健康组和OA组具有高灵敏度，最显著的差异来自上升减速阶段的峰值大小值，基于图像的AE特征轮廓视觉显示中存在明显的数量和强度差异，这是由于OA膝关节产生的AE爆发明显更多，峰值大小值和平均信号水平更高，并且在主成分形成的空间中有两个明显分开的聚类。这些结果为进一步开发AE作为一种新型工具提供了充分支持，以便在临床和家庭环境中促进膝关节动态完整性评估。
