基于VAG信号分析的无创膝关节损伤病变检测与辅助诊断
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摘要
膝关节摆动(VAG)信号是膝关节在做屈伸运动时由于接触摩擦产生的振动信号,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。本研究分析了国内外文献对膝关节摆动信号的研究方法,包括信号的预处理方法、特征提取方法和分类方法几个方面。无创膝关节摆动信号的检测与分析,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,正逐步得到临床医学的重视。最后分析了对于膝关节摆动信号研究还需要解决的问题以及未来的发展方向。
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. In this paper,research methods of vibroarthrographic signal analysis are analyzed and summarized,including signal preprocessing methods,feature extraction methods and classification methods. Such a noninvasive vibroarthrographic signal detection and analysis method is very important for noninvasive detection and auxiliary diagnosis of knee joint injury,and it is gradually getting the attention of clinical medicine. Finally,the problems to be solved and development direction on VAG signal analysis and its applications are analyzed.
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. In this paper,research methods of vibroarthrographic signal analysis are analyzed and summarized,including signal preprocessing methods,feature extraction methods and classification methods. Such a noninvasive vibroarthrographic signal detection and analysis method is very important for noninvasive detection and auxiliary diagnosis of knee joint injury,and it is gradually getting the attention of clinical medicine. Finally,the problems to be solved and development direction on VAG signal analysis and its applications are analyzed.
引文
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[14]BAczkowicz D,Majorczyk E.Joint motion quality in vibroacoustic signal analysis for patients with patellofemoral joint disorders[J].BMC Musculoskeletal Disorders,2014,15(1):426.
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[17]Luo X,Chen P,Yang S,et al.Identification of abnormal knee joint vibroarthrographic signals based on fluctuation features[C]//Biomedical Engineering and Informatics(BMEI),2014 7th International Conference on.IEEE,2014:318-322.
[18]李昕,孙小棋,齐晓英,等.面向心理压力评估的脑电信号多重分形去趋势波动分析方法研究[J].生物医学工程学杂志,2017,34(2):180-187.
[19]Wu Y,Chen P,Luo X,et al.Quantification of knee vibroarthrographic signal irregularity associated with patellofemoral joint cartilage pathology based on entropy and envelope amplitude measures[J].Computer Methods and Programs in Biomedicine,2016,130:1-12.
[20]Nalband S,Sundar A,Prince A A,et al.Feature selection and classification methodology for the detection of knee-joint disorders[J].Computer Methods and Programs in Biomedicine,2016,127(c):94-104.
[21]赵云冬,季忠,彭承琳,等.基于小波变换的心阻抗微分信号去噪及特征点检测研究[J].生物医学工程学杂志,2015,32(2):284-289.
[22]Wu Y,Krishnan S.An adaptive classifier fusion method for analysis of knee-joint vibroarthrographic signals[C]//IEEE International Conference on Computational Intelligence for Measurement Systems and Applications,IEEE,2009:190-193.
[23]Wu Y,Cai S,Yang S,et al.Classification of knee joint vibration signals using bivariate feature distribution estimation and maximal posterior probability decision criterion[J].Entropy,2013,15(4):1375-1387.
[2]Zhu Z,Laslett L L,Han W,et al.Associations between MRI-detected early osteophytes and knee structure in older adults:a population-based cohort study[J].Osteoarthritis and Cartilage,2017,25(12):2055-2062.
[3]Hirvasniemi J,Thevenot J,Multanen J,et al.Association between radiography-based subchondral bone structure and MRI-based cartilage composition in postmenopausal women with mild osteoarthritis[J].Osteoarthritis and Cartilage,2017,25(12):2039-2046.
[4]Wu Y.Knee joint vibroarthrographic signal processing and analysis[M].Springerbriefs in Bioengineering,2015.
[5]Walters C F.Thevalueof jointauscultation[J].The Lancet,1929,213(5514):920-921.
[6]Kernohan W G,Beverland D E,Mccoy G F,et al.The diagnostic potential of vibration arthrography[J].Clinical Orthopaedics and Related Research,1986,210:106-112.
[7]Zhang Y T,Rangayyan R M.Adaptive cancellation of muscle contraction interference in vibroarthrographic signals[J].IEEE Transactions on Biomedical Engineering,1994,41(2):181-191.
[8]Wu Y,Yang S,Zheng F,et al.Removal of artifacts in knee joint vibroarthrographic signals using ensemble empirical mode decomposition and detrended fluctuation analysis[J].Physiological Measurement,2014,35(3):429-439.
[9]Sundar A,Pahwa V,Das C.A new method for denoising knee joint vibroarthrographic signals[C]//India Conference.IEEE,2016:1-5.
[10]Sood S,Kumar M,Pachori R B,et al.Application of empirical mode decomposition based features for analysis of normal and cad heart rate signals[J].Journal of Mechanics in Medicine and Biology,2016,16(01):1640002.
[11]Nalband S,Sreekrishna R R,Prince A A.Analysis of knee joint vibration signals using ensemble empirical mode decomposition[J].Procedia Computer Science,2016,89:820-827.
[12]Tanaka N,Hoshiyama M.Vibroarthrography in patients with knee arthropathy[J].Journal of Back and Musculoskeletal Rehabilitation,2012,25(2):117-122.
[13]Krishnan S,Rangayyan R M,Bell G D,et al.Adaptive time-frequency analysis of knee joint vibroarthrographic signals for noninvasive screening of articular cartilage pathology[J].IEEE Transactions on Biomedical Engineering,2000,47(6):773-783.
[14]BAczkowicz D,Majorczyk E.Joint motion quality in vibroacoustic signal analysis for patients with patellofemoral joint disorders[J].BMC Musculoskeletal Disorders,2014,15(1):426.
[15]Addition I.Age-related impairment of quality of joint motion in vibroarthrographic signal analysis[J].Bio Med Research International,2015:591707.
[16]Rangayyan R M,Oloumi F,Wu Y,et al.Fractal analysis of knee-joint vibroarthrographic signals via power spectral analysis[J].Biomedical Signal Processing and Control,2013,8(1):23-29.
[17]Luo X,Chen P,Yang S,et al.Identification of abnormal knee joint vibroarthrographic signals based on fluctuation features[C]//Biomedical Engineering and Informatics(BMEI),2014 7th International Conference on.IEEE,2014:318-322.
[18]李昕,孙小棋,齐晓英,等.面向心理压力评估的脑电信号多重分形去趋势波动分析方法研究[J].生物医学工程学杂志,2017,34(2):180-187.
[19]Wu Y,Chen P,Luo X,et al.Quantification of knee vibroarthrographic signal irregularity associated with patellofemoral joint cartilage pathology based on entropy and envelope amplitude measures[J].Computer Methods and Programs in Biomedicine,2016,130:1-12.
[20]Nalband S,Sundar A,Prince A A,et al.Feature selection and classification methodology for the detection of knee-joint disorders[J].Computer Methods and Programs in Biomedicine,2016,127(c):94-104.
[21]赵云冬,季忠,彭承琳,等.基于小波变换的心阻抗微分信号去噪及特征点检测研究[J].生物医学工程学杂志,2015,32(2):284-289.
[22]Wu Y,Krishnan S.An adaptive classifier fusion method for analysis of knee-joint vibroarthrographic signals[C]//IEEE International Conference on Computational Intelligence for Measurement Systems and Applications,IEEE,2009:190-193.
[23]Wu Y,Cai S,Yang S,et al.Classification of knee joint vibration signals using bivariate feature distribution estimation and maximal posterior probability decision criterion[J].Entropy,2013,15(4):1375-1387.