Comput Math Methods Med - Knee joint vibration signal analysis with matching pursuit decomposition and dynamic weighted classifier fusion.

Tópicos

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Resumo

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.

Resumo Limpo

analysi knee joint vibrat vag signal can provid quantit indic detect knee joint patholog earli stage addit statist featur develop relat previous studi extract two separ featur number atom deriv wavelet match pursuit decomposit number signific signal turn detect fix threshold time domain perform better classif data set vag signal appli novel classifi fusion system base dynam weight fusion dwf method amelior classif perform comparison singl leastsquar support vector machin lssvm bag ensembl use classif task well result term overal accuraci percentag area receiv oper characterist curv obtain dwfbase classifi fusion method reach respect demonstr effect superior dwf method two distinct featur vag signal analysi

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