Classification of Knee Joint Vibroarthrographic Signals Using k-Nearest Neighbor Algorithm

被引：0

作者：

Liu, Kaizhi ^{[1
]}

Luo, Xin ^{[1
]}

Yang, Shanshan ^{[1
]}

Cai, Suxian ^{[1
]}

Zheng, Fang ^{[1
]}

Wu, Yunfeng ^{[1
]}

机构：

[1] Xiamen Univ, Sch Informat Sci & Technol, Xiamen 361005, Fujian, Peoples R China

来源：

2014 IEEE 27TH CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (CCECE) | 2014年

关键词：

NONINVASIVE DIAGNOSIS; VIBRATION SIGNALS;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页数：4

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