基于多重分形的膝关节摆动信号特征提取与分类  

Feature Extraction and Classification of Vibroarthrographic Signal Based on Multifractal

作　　者：徐一平[1] 邱天爽[1] 刘宇鹏[2] 

机构地区：[1]大连理工大学电子信息与电气工程学部,大连116024 [2]大连大学附属中山医院,大连116001

出　　处：《信号处理》2017年第3期383-388,共6页Journal of Signal Processing

基　　金：国家自然科学基金资助项目(61172108;61139001;81241059;61671105)

摘　　要：膝关节摆动(VAG)信号是膝关节在屈伸活动时由于接触摩擦所产生的振动,它能够反映髌骨软化症、半月板损伤和交叉韧带损伤等膝关节损伤疾病的特征与状态,正逐步得到临床医学的重视。本文依据多重分形去趋势波动方法,定量分析了正常和异常VAG信号的特性,提取了分形标度指数、多重分形谱极值点、广义分形维数和时频信息熵值等特征信息,并采用支持向量机对正常和异常VAG信号进行分类,得到较高的分类准确率,对于膝关节损伤疾病的无创检测和辅助诊断具有重要意义。The vibroarthrographic（ VAG） signal is the vibration and contact friction generated by the knee joint during flexion and extension. It reflects the characteristics and status of chondromalacia,meniscal tears and ruptured ligament and other diseases,and it is gradually getting the attention of clinical medicine. In this paper,the characteristics of normal and abnormal VAG signals are quantitatively analyzed according to the multifractal detrended fluctuation method. The features of the VAG signals are extracted,including the fractal scale exponent,the multifractal spectrum and extremum values,the generalized fractal dimension and the time-frequency information entropy. SVM is used to classify the normal and abnormal VAG signals,getting a high classification accuracy,which is very important for noninvasive detection and assistant diagnosis of knee joint injury.

关 键 词：膝关节摆动信号 多重分形 时频信息熵 支持向量机 

分 类 号：R318.04[医药卫生—生物医学工程]