基于VAG信号的膝关节疾病无创检测与分类的研究进展  

Research progress of noninvasive detection and classification of knee joint diseases based on VAG signal

作　　者：杨佳 邱天爽 刘宇鹏[2] 常世杰 史凯元 YANG Jia;QIU Tian-shuang;LIU Yu-peng;CHANG Shi-jie;SHI Kai-yuan(Faculty of Electronic Information and Electrical Engineering,Dalian University of Technology,Dalian 116024,Liaoning Province,China;Affiliated Zhongshan Hospital of Dalian University,Dalian 116001,Liaoning Province,China)

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

出　　处：《医疗卫生装备》2021年第7期91-96,共6页Chinese Medical Equipment Journal

基　　金：国家自然科学基金项目(61671105,61172108,61139001,81241059)。

摘　　要：介绍了膝关节疾病的分类与膝关节摆动(vibroarthographic,VAG)信号在膝关节疾病无创检测和辅助诊断中发挥的作用,综述了VAG信号的提出和采集、预处理、特征提取和分类识别等方面的研究进展,分析了现阶段基于VAG信号的检测方法仍需解决的问题。指出了随着计算机技术的发展,将人工智能、大数据中的分析方法应用于VAG信号辅助诊断膝关节疾病是未来的研究方向和发展趋势。The classification of knee disorders and the role of the vibroarthographic(VAG)signal in noninvasive detection and auxiliary diagnosis of knee disorders were presented.The research progresses on VAG signal were reviewed in proposal and acquisition,pre-processing,feature extraction and classification recognition,and the problems of VAG signal-based detection methods at the current stage were analyzed.It was pointed out that with the development of computer technology the analysis methods based on artificial intelligence and big data would be involved in VAG signal-assisted diagnosis of knee disorders in the future.

关 键 词：膝关节 膝关节摆动信号 时频分析 非线性分析 深度学习 

分 类 号：R318[医药卫生—生物医学工程] R681.8[医药卫生—基础医学]