Knee osteoarthritis detection based on the combination of empirical mode decomposition and wavelet analysis
Knee osteoarthritis detection based on the combination of empirical mode decomposition and wavelet analysis
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DOI：
10.1299/jbse.20-00017
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发表时间：
2020
影响因子：
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通讯作者：
Rui Gong;K. Hase;Hiroaki Goto;Keisuke Yoshioka;S. Ota
中科院分区：
文献类型：
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作者：
Rui Gong;K. Hase;Hiroaki Goto;Keisuke Yoshioka;S. Ota
膝关节骨关节炎（OA）的早期阶段通常是无症状的。然而，及时发现骨关节炎可以通过适当的运动处方和行为改变来防止进一步的软骨退化。在这篇文章中，一个非侵入性的方法来诊断OA的膝关节记录膝盖振动关节造影（VAG）信号在髌骨中部在站立运动的建议。提出了一种将经验模式分解（EMD）和小波变换相结合的方法来分析非平稳VAG信号。使用Kellgren和Lawrence分级系统III和IV（KLGS III和IV），使用作为一种支持向量机的最小二乘支持向量机算法（LSSVM）对从健康受试者和患有膝OA的患者收集的膝关节VAG信号（26个正常和25个异常）进行分类。LSSVM分类器在区分正常和异常受试者方面达到了86.67%的准确率，证明了自相关函数特征和连续小波变换（CWT）特征的有效性。因此，VAG信号对于健康受试者和OA受试者的分类具有临床意义。
The early-stage of knee osteoarthritis (OA) is usually asymptomatic. However, timely detection of osteoarthritis can prevent further cartilage degeneration via appropriate exercise prescription and behavioral change. In this article, a noninvasive method to diagnose the OA of a knee recording the knee vibroarthrographic (VAG) signals over the mid-patella during the standing movement is proposed. A method that combines empirical mode decomposition (EMD) and wavelet transform is developed to analyze the nonstationary VAG signals. The least squares support vector machine algorithm (LSSVM) that is a type of support vector machine is used to classify the knee joint VAG signals (26 normal and 25 abnormal) collected from healthy subjects and patients suffering from the knee OA using the Kellgren and Lawrence grading system III and IV (KLGS III and IV). The LSSVM classifier achieves an accuracy of 86.67% in differentiating the normal and abnormal subjects that proves the effectiveness of the autocorrelation function features and continuous wavelet transform (CWT) features. Therefore, the VAG signals can be clinically significant for the classification of healthy and OA subjects.