Rangayyan Rangaraj M, Wu Yunfeng
Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada.
Ann Biomed Eng. 2009 Jan;37(1):156-63. doi: 10.1007/s10439-008-9601-1. Epub 2008 Nov 18.
Knee-joint sounds or vibroarthrographic (VAG) signals contain diagnostic information related to the roughness, softening, breakdown, or the state of lubrication of the articular cartilage surfaces. Objective analysis of VAG signals provides features for pattern analysis, classification, and noninvasive diagnosis of knee-joint pathology of various types. We propose parameters related to signal variability for the analysis of VAG signals, including an adaptive turns count and the variance of the mean-squared value computed during extension, flexion, and a full swing cycle of the leg, for the purpose of classification as normal or abnormal, that is, screening. With a database of 89 VAG signals, screening efficiency of up to 0.8570 was achieved, in terms of the area under the receiver operating characteristics curve, using a neural network classifier based on radial-basis functions, with all of the six proposed features. Using techniques for feature selection, the turns counts for the flexion and extension parts of the VAG signals were chosen as the top two features, leading to an improved screening efficiency of 0.9174. The proposed methods could lead to objective criteria for improved selection of patients for clinical procedures and reduce healthcare costs.
膝关节声音或振动关节造影（VAG）信号包含与关节软骨表面的粗糙度、软化、破损或润滑状态相关的诊断信息。对VAG信号进行客观分析可为各种类型的膝关节病变的模式分析、分类和无创诊断提供特征。我们提出了与信号变异性相关的参数，用于分析VAG信号，包括自适应转折计数以及在腿部伸展、弯曲和完整摆动周期期间计算的均方值的方差，目的是将其分类为正常或异常，即进行筛查。在一个包含89个VAG信号的数据库中，使用基于径向基函数的神经网络分类器，结合所有六个提出的特征，在接收器操作特征曲线下的面积方面，实现了高达0.8570的筛查效率。使用特征选择技术，VAG信号的弯曲和伸展部分的转折计数被选为前两个特征，从而将筛查效率提高到0.9174。所提出的方法可能会导致客观标准，以改进临床程序中患者的选择并降低医疗成本。
