Associate professor
Supervisor of Master's Candidates
E-Mail:
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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DOI number:10.1016/j.cmpb.2016.03.021
Journal:Computer Methods and Programs in Biomedicine
Abstract:Background and objective: Injury of knee joint cartilage may result in pathological vibrations between the articular surfaces during extension and flexion motions. The aim of this paper is to analyze and quantify vibroarthrographic (VAG) signal irregularity associated with articular cartilage degeneration and injury in the patellofemoral joint. Methods: The symbolic entropy (SyEn), approximate entropy (ApEn), fuzzy entropy (FuzzyEn), and the mean, standard deviation, and root-mean-squared (RMS) values of the envelope amplitude, were utilized to quantify the signal fluctuations associated with articular cartilage pathology of the patellofemoral joint. The quadratic discriminant analysis (QDA), generalized logistic regression analysis (GLRA), and support vector machine (SVM) methods were used to perform signal pattern classifications. Results: The experimental results showed that the patients with cartilage pathology (CP) possess larger SyEn and ApEn, but smaller FuzzyEn, over the statistical significance level of the Wilcoxon rank-sum test (p < 0.01), than the healthy subjects (HS). The mean, standard deviation, and RMS values computed from the amplitude difference between the upper and lower signal envelopes are also consistently and significantly larger (p < 0.01) for the group of CP patients than for the HS group. The SVM based on the entropy and envelope amplitude features can provide superior classification performance as compared with QDA and GLRA, with an overall accuracy of 0.8356, sensitivity of 0.9444, specificity of 0.8, Matthews correlation coefficient of 0.6599, and an area of 0.9212 under the receiver operating characteristic curve. Conclusions: The SyEn, ApEn, and FuzzyEn features can provide useful information about pathological VAG signal irregularity based on different entropy metrics. The statistical parameters of signal envelope amplitude can be used to characterize the temporal fluctuations related to the cartilage pathology. (C) 2016 Elsevier Ireland Ltd. All rights reserved.
Co-author:Pinnan Chen,Xin Luo,Hui Huang,Lifang Liao,Yuchen Yao,Meihong Wu,Rangaraj M. Rangayyan
First Author:Yunfeng Wu*
Indexed by:Article
Volume:130
Issue:1
Page Number:1-12
Translation or Not:no
Date of Publication:2016-07-01
Included Journals:SCI
Links to published journals:https://doi.org/10.1016/j.cmpb.2016.03.021