Abstract：
OBJECTIVE: Knee-joint vibroarthrographic (VAG) signal is an effective method for performing a non-invasive knee osteoarthritis (KOA) diagnosis, VAG signal analysis plays a crucial role in achieving the early pathological screening of the knee joint. In order to improve the accuracy of knee pathology screening and to investigate the method suitable for embedded in wearable diagnostic device for knee joint, this paper proposes a knee pathology screening method. Aiming to fill the gap of lacking suitable and unified evaluation indexes for single feature and fusion feature, this paper proposes feature separability evaluation criteria.
METHODS: In this paper, we propose a knee joint pathology screening method based on feature fusion and dimension reduction combined with random forest classifier, as well as, the evaluation criteria of feature separability. As for pathological screening method, this paper proposes the idea of multi-dimensional feature fusion, using principal component analysis (PCA) to reduce the redundant part of fusion feature (F-F) to obtain deep fusion feature (D-F-F) with more separability. Meanwhile, this paper proposes the maximal information coefficient (MIC) and correlation matrix collinearity (CMC) feature evaluation criteria, these not only can be used as new feature quantitative metrics, but also illustrate that the divisibility of the deep fusion feature is more potent than that before feature dimension reduction.
RESULTS: The experimental results show that the method in this paper has good performance in pathology classification on random forest classifier with 96% accuracy, especially the accuracy of SVM and K-NN are also improved after feature dimension reduction.
CONCLUSIONS: The results indicate that this classification research has high screening efficiency for KOA diagnosis and could provide a feasible method for computer-assisted non-invasive diagnosis of KOA. And we provide a novel way for separability evaluation of VAG signal features.
METHODS: In this paper, we propose a knee joint pathology screening method based on feature fusion and dimension reduction combined with random forest classifier, as well as, the evaluation criteria of feature separability. As for pathological screening method, this paper proposes the idea of multi-dimensional feature fusion, using principal component analysis (PCA) to reduce the redundant part of fusion feature (F-F) to obtain deep fusion feature (D-F-F) with more separability. Meanwhile, this paper proposes the maximal information coefficient (MIC) and correlation matrix collinearity (CMC) feature evaluation criteria, these not only can be used as new feature quantitative metrics, but also illustrate that the divisibility of the deep fusion feature is more potent than that before feature dimension reduction.
RESULTS: The experimental results show that the method in this paper has good performance in pathology classification on random forest classifier with 96% accuracy, especially the accuracy of SVM and K-NN are also improved after feature dimension reduction.
CONCLUSIONS: The results indicate that this classification research has high screening efficiency for KOA diagnosis and could provide a feasible method for computer-assisted non-invasive diagnosis of KOA. And we provide a novel way for separability evaluation of VAG signal features.
摘要：
目的：膝关节振动关节造影（VAG）信号是进行非侵入性膝骨关节炎（KOA）诊断的有效方法，VAG信号分析在实现膝关节早期病理筛查中起着至关重要的作用。为了提高膝关节病理筛查的准确性,研究适合嵌入可穿戴膝关节诊断装置的方法,本文提出了一种膝关节病理筛查方法。旨在填补单一特征和融合特征缺乏合适统一评价指标的空白,本文提出了特征可分性评价标准。
方法：在本文中，提出了一种基于特征融合和降维结合随机森林分类器的膝关节病理筛选方法,还有,特征可分性的评价标准。至于病理筛查方法，本文提出了多维特征融合的思想,利用主成分分析(PCA)来减少融合特征(F-F)的冗余部分,得到具有更高可分性的深度融合特征(D-F-F)。同时,本文提出了最大信息系数(MIC)和相关矩阵共线性(CMC)特征评价准则,这些不仅可以用作新的特征量化指标，但也说明了深度融合特征的可分性比特征降维之前更有效。
结果：实验结果表明，本文方法在随机森林分类器上的病理分类中具有良好的性能，准确率为96％，特别是特征降维后SVM和K-NN的精度也得到了提高。
结论：本分类研究对KOA的诊断具有较高的筛查效率，可为计算机辅助KOA的无创性诊断提供一种可行的方法。并且我们为VAG信号特征的可分性评估提供了一种新颖的方法。
方法：在本文中，提出了一种基于特征融合和降维结合随机森林分类器的膝关节病理筛选方法,还有,特征可分性的评价标准。至于病理筛查方法，本文提出了多维特征融合的思想,利用主成分分析(PCA)来减少融合特征(F-F)的冗余部分,得到具有更高可分性的深度融合特征(D-F-F)。同时,本文提出了最大信息系数(MIC)和相关矩阵共线性(CMC)特征评价准则,这些不仅可以用作新的特征量化指标，但也说明了深度融合特征的可分性比特征降维之前更有效。
结果：实验结果表明，本文方法在随机森林分类器上的病理分类中具有良好的性能，准确率为96％，特别是特征降维后SVM和K-NN的精度也得到了提高。
结论：本分类研究对KOA的诊断具有较高的筛查效率，可为计算机辅助KOA的无创性诊断提供一种可行的方法。并且我们为VAG信号特征的可分性评估提供了一种新颖的方法。
