A method of feature fusion and dimension reduction for knee joint pathology screening and separability evaluation criteria. (September 2022) - Ma, Chunyi; Yang, Jingyi; Wang, Qian; Liu, Hao; Xu, Hu; Ding, Tan; Yang, Jianhua
Highlights: We proposed the idea of multi-dimensional feature fusion to jointly characterize signal features from multiple perspectives, combine with dimensionality reduction methods to remove redundant features, and mine deep features of signals from shallow features to obtain signal features with stronger differentiability. We solved the problem that traditional feature extraction methods only find shallow features and existing redundant features lead to weak feature separability for KOA screening. The pathology screening method proposed in this paper is simple and high accuracy, avoiding complex signal transformations and having the ability to handle large data efficiently. The pathology screening of 732 sets of VAG data is achieved by our method, which is helpful for embedding the algorithm into the portable knee pathology diagnostic device for real-time KOA diagnosis. We propose two novel feature evaluation criteria for judging the separability of single features and fusion features, it fills the gap of lacking suitable and unified feature quantitative evaluation index. Abstract: Background and 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. Conclusion: 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.