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

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	Classification of Knee Joint Vibration Signals Using Bivariate Feature Distribution Estimation and Maximal Posterior Probability Decision Criterion
	Wu, Yunfeng ; Cai, Suxian ; Yang, Shanshan ; Zheng, Fang ; Xiang, Ning ; Wu YF(吴云峰)
刊名	http://dx.doi.org/10.3390/e15041375
	2013
关键词	TIME-FREQUENCY ANALYSIS RADIAL-BASIS FUNCTIONS VIBROARTHROGRAPHIC SIGNALS CARTILAGE PATHOLOGY DENSITY-FUNCTION DIAGNOSIS ALGORITHM
英文摘要	National Natural Science Foundation of China [81101115, 31200769]; Natural Science Foundation of Fujian Province of China [2011J01371]; Fundamental Research Funds for the Central Universities of China [2010121061, 2010121062]; Xiamen University Undergraduate Innovation Training Project [XDDC201210384072]; Fundamental Research Funds for the Central Universities [CXB2011023]; 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 (A(z)) 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%, A(z): 0.8564) or the support vector machine with polynomial kernels (accuracy: 81.33%, A(z): 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.
语种	英语
出版者	MDPI AG
内容类型	期刊论文
源URL	[http://dspace.xmu.edu.cn/handle/2288/92581]
专题	信息技术－已发表论文
推荐引用方式 GB/T 7714	Wu, Yunfeng,Cai, Suxian,Yang, Shanshan,et al. Classification of Knee Joint Vibration Signals Using Bivariate Feature Distribution Estimation and Maximal Posterior Probability Decision Criterion[J]. http://dx.doi.org/10.3390/e15041375,2013.
APA	Wu, Yunfeng,Cai, Suxian,Yang, Shanshan,Zheng, Fang,Xiang, Ning,&吴云峰.(2013).Classification of Knee Joint Vibration Signals Using Bivariate Feature Distribution Estimation and Maximal Posterior Probability Decision Criterion.http://dx.doi.org/10.3390/e15041375.
MLA	Wu, Yunfeng,et al."Classification of Knee Joint Vibration Signals Using Bivariate Feature Distribution Estimation and Maximal Posterior Probability Decision Criterion".http://dx.doi.org/10.3390/e15041375 (2013).