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	Classification of knee joint vibroarthrographic signals using k-nearest neighbor algorithm
	Liu, Kaizhi ; Luo, Xin ; Yang, Shanshan ; Cai, Suxian ; Zheng, Fang ; Wu, Yunfeng ; Wu YF(吴云峰)
	2014
关键词	Algorithms Discriminant analysis Motion compensation Pattern recognition
英文摘要	Conference Name:2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering, CCECE 2014. Conference Address: Toronto, ON, Canada. Time:May 4, 2014 - May 7, 2014.; et al.; IEEE Canada - Canada Central Area; IEEE Canada - IEEE Kitchener - Waterloo Section; IEEE London Section; IEEE Peterborough Section; IEEE Toronto Section; The pathological condition in a degenerative knee joint may be assessed by analyzing the knee joint vibroarthrographic signals. With the severity level of the knee joint disorders evaluated by the computational methods, unnecessary imaging examination or open surgery can be prevented. In the present study, we used the k-nearest neighbor (k-NN) algorithm, a type of lazy learning approach, to classify the knee joint vibroarthrographic signals collected from healthy subjects and symptomatic patients with knee joint disorders. With the representative features of form factor and variance of the mean-square values, the k-NN algorithm is able to correctly discriminate 80% signals with the sensitivity of 0.71 and the specificity of 0.85, which is superior to the total accurate rate of 77% (sensitivity: 0.64, specificity: 0.85) provided by the Fisher's linear discriminant analysis. ? 2014 IEEE.
语种	英语
出处	http://dx.doi.org/10.1109/CCECE.2014.6900933
出版者	Institute of Electrical and Electronics Engineers Inc.
内容类型	其他
源URL	[http://dspace.xmu.edu.cn/handle/2288/86943]
专题	信息技术－会议论文
推荐引用方式 GB/T 7714	Liu, Kaizhi,Luo, Xin,Yang, Shanshan,et al. Classification of knee joint vibroarthrographic signals using k-nearest neighbor algorithm. 2014-01-01.

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