Quantification of knee vibroarthrographic signal irregularity associated with patellofemoral joint cartilage pathology based on entropy and envelope amplitude measures. Issue 130 (July 2016)

• Record Type: Journal Article
• Authors: Wu, Yunfeng; Chen, Pinnan; Luo, Xin; Huang, Hui; Liao, Lifang; Yao, Yuchen; Wu, Meihong; Rangayyan, Rangaraj M.
• Is Part Of: Computer methods and programs in biomedicine. Issue 130(2016)
• Page Start: 1
• Page End: 12
• Publication Date: 2016-07
• Subjects: Approximate entropy -- Articular cartilage -- Fuzzy entropy -- Knee joint -- Symbolic entropy -- Vibroarthrography; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2016.03.021
• Languages: English
• ISSNs: 0169-2607

Abstract

Abstract : Graphical abstract: Abstract : Highlights: Symbolic entropy, approximate entropy, and fuzzy entropy are used to measure the irregularity of knee vibroarthrographic signals. Mean, standard deviation, and root-mean-squared value parameters of the signal envelope amplitude are computed to characterize the signal fluctuations. Gender-dependent statistics of entropy and envelope amplitude parameters are discussed. Abstract: 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.