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中文
Yunfeng Wu

Associate professor

Supervisor of Master's Candidates


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Date of Employment:2009-12-14

School/Department:信息学院

Education Level:博士研究生毕业

Business Address:厦门大学翔安校区西部片区6号楼(睿信楼)202室

Gender:Male

Contact Information:yunfengwu@xmu.edu.cn

Degree:Doctor of Engineering (D.Eng.)

Status:在职

Alma Mater:北京邮电大学

Discipline:生物医学工程
信号与信息处理

Academic Honor:

2013   Outstanding talents in the new century

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

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DOI number:10.1016/j.medengphy.2014.07.008

Journal:Medical Engineering & Physics

Abstract: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.

Co-author:Suxian Cai,Fang Zheng,Kaizhi Liu,Meihong Wu,Quan Zou,Jian Chen

First Author:Shanshan Yang

Indexed by:Article

Correspondence Author:Yunfeng Wu*

Volume:36

Issue:10

Page Number:1305-1311

Translation or Not:no

Date of Publication:2014-10-01

Included Journals:SCI

Links to published journals:https://doi.org/10.1016/j.medengphy.2014.07.008