NDLI: Classification of knee-joint vibroarthrographic signals using time-domain and time-frequency domain features and least-squares support vector machine

Content Provider	IEEE Xplore Digital Library
Author	Yunfeng Wu Krishnan, S.
Copyright Year	2009
Description	Author affiliation: Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, M5B 2K3, Canada (Yunfeng Wu; Krishnan, S.)
Abstract	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.
Starting Page	1
Ending Page	6
File Size	413811
Page Count	6
File Format	PDF
ISBN	9781424432974
DOI	10.1109/ICDSP.2009.5201156
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-07-05
Publisher Place	Greece
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Time domain analysis Time frequency analysis Support vector machines Support vector machine classification Matching pursuit algorithms Signal analysis Wavelet analysis Pathology Wavelet domain Data mining Support vector machine Knee-joint vibration sounds Wavelets Matching pursuit Turns count
Content Type	Text
Resource Type	Article

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