Quantification of knee vibroarthrographic signal irregularity associated with patellofemoral joint cartilage pathology based on entropy and envelope amplitude measures

BACKGROUND AND OBJECTIVE Injury of knee joint cartilage may result in pathological vibrations between the articular surfaces during extension and flexion motions. The aim of this paper is to analyze and quantify vibroarthrographic (VAG) signal irregularity associated with articular cartilage degeneration and injury in the patellofemoral joint. METHODS The symbolic entropy (SyEn), approximate entropy (ApEn), fuzzy entropy (FuzzyEn), and the mean, standard deviation, and root-mean-squared (RMS) values of the envelope amplitude, were utilized to quantify the signal fluctuations associated with articular cartilage pathology of the patellofemoral joint. The quadratic discriminant analysis (QDA), generalized logistic regression analysis (GLRA), and support vector machine (SVM) methods were used to perform signal pattern classifications. RESULTS The experimental results showed that the patients with cartilage pathology (CP) possess larger SyEn and ApEn, but smaller FuzzyEn, over the statistical significance level of the Wilcoxon rank-sum test (p<0.01), than the healthy subjects (HS). The mean, standard deviation, and RMS values computed from the amplitude difference between the upper and lower signal envelopes are also consistently and significantly larger (p<0.01) for the group of CP patients than for the HS group. The SVM based on the entropy and envelope amplitude features can provide superior classification performance as compared with QDA and GLRA, with an overall accuracy of 0.8356, sensitivity of 0.9444, specificity of 0.8, Matthews correlation coefficient of 0.6599, and an area of 0.9212 under the receiver operating characteristic curve. CONCLUSIONS The SyEn, ApEn, and FuzzyEn features can provide useful information about pathological VAG signal irregularity based on different entropy metrics. The statistical parameters of signal envelope amplitude can be used to characterize the temporal fluctuations related to the cartilage pathology.

[1]  Tsair-Fwu Lee,et al.  Non-invasive Knee Osteoarthritis Diagnosis via Vibroarthrographic Signal Analysis , 2014, J. Inf. Hiding Multim. Signal Process..

[2]  Ning Xiang,et al.  Classification of Knee Joint Vibration Signals Using Bivariate Feature Distribution Estimation and Maximal Posterior Probability Decision Criterion , 2013, Entropy.

[3]  Karthikeyan Umapathy,et al.  Modified local discriminant bases algorithm and its application in analysis of human knee joint vibration signals , 2006, IEEE Transactions on Biomedical Engineering.

[4]  Minoru Hoshiyama,et al.  ARTICULAR SOUND AND CLINICAL STAGES IN KNEE ARTHROPATHY , 2011 .

[5]  Suxian Cai,et al.  Removal of artifacts in knee joint vibroarthrographic signals using ensemble empirical mode decomposition and detrended fluctuation analysis. , 2014, Physiological measurement.

[6]  Steven C Abbott,et al.  Vibration arthrometry: a critical review. , 2013, Critical reviews in biomedical engineering.

[7]  Rangaraj M. Rangayyan,et al.  Fractal analysis of knee-joint vibroarthrographic signals via power spectral analysis , 2013, Biomed. Signal Process. Control..

[8]  Yunfeng Wu,et al.  Knee Joint Vibroarthrographic Signal Processing and Analysis , 2015 .

[9]  R.M. Rangayyan,et al.  Parametric representation and screening of knee joint vibroarthrographic signals , 1997, IEEE Transactions on Biomedical Engineering.

[10]  Sridhar Krishnan,et al.  Knee Joint Vibration Signal Analysis with Matching Pursuit Decomposition and Dynamic Weighted Classifier Fusion , 2013, Comput. Math. Methods Medicine.

[11]  R. Rangayyan,et al.  Computer-aided diagnosis of knee-joint disorders via vibroarthrographic signal analysis: a review. , 2010, Critical reviews in biomedical engineering.

[12]  Henrik Madsen,et al.  Introduction to General and Generalized Linear Models , 2010 .

[13]  Rangaraj M. Rangayyan,et al.  Analysis of Vibroarthrographic Signals with Features Related to Signal Variability and Radial-Basis Functions , 2008, Annals of Biomedical Engineering.

[14]  Zahra M. K. Maussavi,et al.  Screening of vibroarthrographic signals via adaptive segmentation and linear prediction modeling , 1996, IEEE Transactions on Biomedical Engineering.

[15]  Lei Shi,et al.  Detrending knee joint vibration signals with a cascade moving average filter , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[16]  W. Youden,et al.  Index for rating diagnostic tests , 1950, Cancer.

[17]  Sridhar Krishnan,et al.  Combining least-squares support vector machines for classification of biomedical signals: a case study with knee-joint vibroarthrographic signals , 2011, J. Exp. Theor. Artif. Intell..

[18]  Krzysztof Kręcisz,et al.  Age-Related Impairment of Quality of Joint Motion in Vibroarthrographic Signal Analysis , 2015, BioMed research international.

[19]  N. P. Reddy,et al.  Noninvasive Measurement of Acceleration at the Knee Joint in Patients with Rheumatoid Arthritis and Spondyloarthropathy of the Knee , 2001, Annals of Biomedical Engineering.

[20]  Minoru Hoshiyama,et al.  Vibroarthrography in patients with knee arthropathy. , 2012, Journal of back and musculoskeletal rehabilitation.

[21]  Pierre Baldi,et al.  Assessing the accuracy of prediction algorithms for classification: an overview , 2000, Bioinform..

[22]  S M Pincus,et al.  Approximate entropy as a measure of system complexity. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[23]  A L Goldberger,et al.  Physiological time-series analysis: what does regularity quantify? , 1994, The American journal of physiology.

[24]  Tingting Mu,et al.  Screening of knee-joint vibroarthrographic signals using the strict 2-surface proximal classifier and genetic algorithm , 2008, Comput. Biol. Medicine.

[25]  Rangaraj M. Rangayyan,et al.  Biomedical Signal Analysis , 2015 .

[26]  Weiting Chen,et al.  Measuring complexity using FuzzyEn, ApEn, and SampEn. , 2009, Medical engineering & physics.

[27]  Jin U. Kang,et al.  An enhanced algorithm for knee joint sound classification using feature extraction based on time-frequency analysis , 2009, Comput. Methods Programs Biomed..

[28]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[29]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[30]  Abraham Charnes,et al.  The Equivalence of Generalized Least Squares and Maximum Likelihood Estimates in the Exponential Family , 1976 .

[31]  M C Eguia,et al.  Information transmission and recovery in neural communications channels. , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[32]  Rangaraj M. Rangayyan,et al.  Adaptive time-frequency analysis of knee joint vibroarthrographic signals for noninvasive screening of articular cartilage pathology , 2000, IEEE Transactions on Biomedical Engineering.

[33]  S. Pincus.  Approximate entropy (ApEn) as a complexity measure. , 1995, Chaos.

[34]  Wangxin Yu,et al.  Characterization of Surface EMG Signal Based on Fuzzy Entropy , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[35]  M. Arif,et al.  Complexity analysis of stride interval time series by threshold dependent symbolic entropy , 2006, European Journal of Applied Physiology.

[36]  David T Felson,et al.  Clinical practice. Osteoarthritis of the knee. , 2006, The New England journal of medicine.

[37]  Q. Zou,et al.  Representation of fluctuation features in pathological knee joint vibroarthrographic signals using kernel density modeling method. , 2014, Medical engineering & physics.

[38]  Rangaraj M. Rangayyan,et al.  Screening of knee-joint vibroarthrographic signals using probability density functions estimated with Parzen windows , 2010, Biomed. Signal Process. Control..

[39]  Rangaraj M. Rangayyan,et al.  Screening of knee-joint vibroarthrographic signals using statistical parameters and radial basis functions , 2008, Medical & Biological Engineering & Computing.

[40]  J Goodacre,et al.  Knee acoustic emission: a potential biomarker for quantitative assessment of joint ageing and degeneration. , 2011, Medical engineering & physics.

[41]  William Stafford Noble,et al.  Support vector machine , 2013 .

[42]  Dawid Bączkowicz,et al.  Joint motion quality in vibroacoustic signal analysis for patients with patellofemoral joint disorders , 2014, BMC Musculoskeletal Disorders.