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Open AccessArticle

Classification of Knee Joint Vibration Signals Using Bivariate Feature Distribution Estimation and Maximal Posterior Probability Decision Criterion

School of Information Science and Technology, Xiamen University, 422 Si Ming South Road,Xiamen 361005, China
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Entropy 2013, 15(4), 1375-1387; https://doi.org/10.3390/e15041375
Received: 4 February 2013 / Revised: 8 April 2013 / Accepted: 15 April 2013 / Published: 17 April 2013
(This article belongs to the Special Issue Maximum Entropy and Bayes Theorem)
Analysis of knee joint vibration or vibroarthrographic (VAG) signals using signal processing and machine learning algorithms possesses high potential for the noninvasive detection of articular cartilage degeneration, which may reduce unnecessary exploratory surgery. Feature representation of knee joint VAG signals helps characterize the pathological condition of degenerative articular cartilages in the knee. This paper used the kernel-based probability density estimation method to model the distributions of the VAG signals recorded from healthy subjects and patients with knee joint disorders. The estimated densities of the VAG signals showed explicit distributions of the normal and abnormal signal groups, along with the corresponding contours in the bivariate feature space. The signal classifications were performed by using the Fisher’s linear discriminant analysis, support vector machine with polynomial kernels, and the maximal posterior probability decision criterion. The maximal posterior probability decision criterion was able to provide the total classification accuracy of 86.67% and the area (Az) of 0.9096 under the receiver operating characteristics curve, which were superior to the results obtained by either the Fisher’s linear discriminant analysis (accuracy: 81.33%, Az: 0.8564) or the support vector machine with polynomial kernels (accuracy: 81.33%, Az: 0.8533). Such results demonstrated the merits of the bivariate feature distribution estimation and the superiority of the maximal posterior probability decision criterion for analysis of knee joint VAG signals. View Full-Text
Keywords: knee joint vibration signals; vibration arthrometry; kernel density estimation; linear discriminant analysis; posterior probability; support vector machine knee joint vibration signals; vibration arthrometry; kernel density estimation; linear discriminant analysis; posterior probability; support vector machine
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Wu, Y.; Cai, S.; Yang, S.; Zheng, F.; Xiang, N. Classification of Knee Joint Vibration Signals Using Bivariate Feature Distribution Estimation and Maximal Posterior Probability Decision Criterion. Entropy 2013, 15, 1375-1387.

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