Representation of fluctuation features in pathological knee joint vibroarthrographic signals using kernel density modeling method

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	Representation of fluctuation features in pathological knee joint vibroarthrographic signals using kernel density modeling method
	Yang, Shanshan ; Cai, Suxian ; Zheng, Fang ; Wu, Yunfeng ; Liu, Kaizhi ; Wu, Meihong ; Zou, Quan ; Chen, Jian ; Wu YF(吴云峰) ; Wu MH(吴梅红) ; Zou Q(邹权)
刊名	http://dx.doi.org/10.1016/j.medengphy.2014.07.008
	2014
关键词	Classification (of information) Computer aided analysis Computerized tomography Fractals Signal processing Signal receivers Support vector machines
英文摘要	This article applies advanced signal processing and computational methods to study the subtle fluctuations in knee joint vibroarthrographic (VAG) signals. Two new features are extracted to characterize the fluctuations of VAG signals. The fractal scaling index parameter is computed using the detrended fluctuation analysis algorithm to describe the fluctuations associated with intrinsic correlations in the VAG signal. The averaged envelope amplitude feature measures the difference between the upper and lower envelopes averaged over an entire VAG signal. Statistical analysis with the Kolmogorov-Smirnov test indicates that both of the fractal scaling index (. p=. 0.0001) and averaged envelope amplitude (. p=. 0.0001) features are significantly different between the normal and pathological signal groups. The bivariate Gaussian kernels are utilized for modeling the densities of normal and pathological signals in the two-dimensional feature space. Based on the feature densities estimated, the Bayesian decision rule makes better signal classifications than the least-squares support vector machine, with the overall classification accuracy of 88% and the area of 0.957 under the receiver operating characteristic (ROC) curve. Such VAG signal classification results are better than those reported in the state-of-the-art literature. The fluctuation features of VAG signals developed in the present study can provide useful information on the pathological conditions of degenerative knee joints. Classification results demonstrate the effectiveness of the kernel feature density modeling method for computer-aided VAG signal analysis. ? 2014 IPEM.
语种	英语
出版者	Elsevier Ltd
内容类型	期刊论文
源URL	[http://dspace.xmu.edu.cn/handle/2288/92923]
专题	信息技术－已发表论文
推荐引用方式 GB/T 7714	Yang, Shanshan,Cai, Suxian,Zheng, Fang,et al. Representation of fluctuation features in pathological knee joint vibroarthrographic signals using kernel density modeling method[J]. http://dx.doi.org/10.1016/j.medengphy.2014.07.008,2014.
APA	Yang, Shanshan.,Cai, Suxian.,Zheng, Fang.,Wu, Yunfeng.,Liu, Kaizhi.,...&邹权.(2014).Representation of fluctuation features in pathological knee joint vibroarthrographic signals using kernel density modeling method.http://dx.doi.org/10.1016/j.medengphy.2014.07.008.
MLA	Yang, Shanshan,et al."Representation of fluctuation features in pathological knee joint vibroarthrographic signals using kernel density modeling method".http://dx.doi.org/10.1016/j.medengphy.2014.07.008 (2014).