QQ客服 官方微博 反馈留言

	CLASSIFICATION OF KNEE-JOINT VIBROARTHROGRAPHIC SIGNALS USING TIME-DOMAIN AND TIME-FREQUENCY DOMAIN FEATURES AND LEAST-SQUARES SUPPORT VECTOR MACHINE
	Wu, Y. F. ; Wu YF(吴云峰) ; Krishnan, S.
	2013-12-12
关键词	Knee-joint vibration sounds Wavelets Matching pursuit Turns count Support vector machine
英文摘要	Analysis of knee-joint vibration sounds, also known as vibroarthrographic (VAG) signals, could lead to a noninvasive clinical tool for early detection of knee-joint pathology. In this paper, we employed the wavelet matching pursuit (MP) decomposition and signal variability for time-frequency domain and time-domain analysis of VAG signals. The number of wavelet MP atoms and the number of significant turns detected with the fixed threshold from signal variability analysis were extracted as prominent features for the classification over the data set of 89 VAG signals. Compared with the Fisher linear discriminant analysis, the nonlinear least-squares support vector machine (LS-SVM) is able to achieve higher overall accuracy of 73.03%, and the area of 0.7307 under the receiver operating characteristic curve.
语种	英语
内容类型	期刊论文
源URL	[http://dspace.xmu.edu.cn/handle/2288/70740]
专题	信息技术－已发表论文
推荐引用方式 GB/T 7714	Wu, Y. F.,Wu YF,Krishnan, S.. CLASSIFICATION OF KNEE-JOINT VIBROARTHROGRAPHIC SIGNALS USING TIME-DOMAIN AND TIME-FREQUENCY DOMAIN FEATURES AND LEAST-SQUARES SUPPORT VECTOR MACHINE[J],2013.
APA	Wu, Y. F.,吴云峰,&Krishnan, S..(2013).CLASSIFICATION OF KNEE-JOINT VIBROARTHROGRAPHIC SIGNALS USING TIME-DOMAIN AND TIME-FREQUENCY DOMAIN FEATURES AND LEAST-SQUARES SUPPORT VECTOR MACHINE..
MLA	Wu, Y. F.,et al."CLASSIFICATION OF KNEE-JOINT VIBROARTHROGRAPHIC SIGNALS USING TIME-DOMAIN AND TIME-FREQUENCY DOMAIN FEATURES AND LEAST-SQUARES SUPPORT VECTOR MACHINE".(2013).

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。