title: Knee joint vibration signal analysis with matching pursuit decomposition and dynamic weighted classifier fusion.
abstract: Analysis of knee joint vibration (VAG) signals can provide quantitative indices for detection of knee joint pathology at an early stage. In addition to the statistical features developed in the related previous studies, we extracted two separable features, that is, the number of atoms derived from the wavelet matching pursuit decomposition and the number of significant signal turns detected with the fixed threshold in the time domain. To perform a better classification over the data set of 89 VAG signals, we applied a novel classifier fusion system based on the dynamic weighted fusion (DWF) method to ameliorate the classification performance. For comparison, a single leastsquares support vector machine (LS-SVM) and the Bagging ensemble were used for the classification task as well. The results in terms of overall accuracy in percentage and area under the receiver operating characteristic curve obtained with the DWF-based classifier fusion method reached 88.76% and 0.9515, respectively, which demonstrated the effectiveness and superiority of the DWF method with two distinct features for the VAG signal analysis.
authors.affiliation: School of Information Science and Technology, Xiamen University, 422 Si Ming South Road, Xiamen, Fujian 361005, China.
authors.forename: Suxian
authors.initials: S
authors.lastname: Cai
authors.forename: Shanshan
authors.initials: S
authors.lastname: Yang
authors.forename: Fang
authors.initials: F
authors.lastname: Zheng
authors.forename: Meng
authors.initials: M
authors.lastname: Lu
authors.forename: Yunfeng
authors.initials: Y
authors.lastname: Wu
authors.forename: Sridhar
authors.initials: S
authors.lastname: Krishnan
mesh_terms: D000465:Algorithms; D001696:Biomechanical Phenomena; D002357:Cartilage Diseases / Q000473:pathology; D002358:Cartilage, Articular / Q000473:pathology; D019295:Computational Biology / Q000379:methods; D006801:Humans; D007719:Knee Joint / Q000033:anatomy & histology* / Q000473:pathology; D016018:Least-Squares Analysis; D015233:Models, Statistical; D010003:Osteoarthritis / Q000503:physiopathology; D010363:Pattern Recognition, Automated; D012372:ROC Curve; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine; D014732:Vibration