Classification of Knee Joint Vibration Signals Using Bivariate Feature Distribution Estimation and Maximal Posterior Probability Decision Criterion

Yunfeng Wu,Shanshan Yang,Fang Zheng,Ning Xiang,Suxian Cai

doi:10.3390/e15041375

Abstract

Analysis of knee joint vibration or vibroarthrographic (VAG) signals using signal processing and machine learning algorithms possesses high potential for the noninvasive detection of articular cartilage degeneration, which may reduce unnecessary exploratory surgery. Feature representation of knee joint VAG signals helps characterize the pathological condition of degenerative articular cartilages in the knee. This paper used the kernel-based probability density estimation method to model the distributions of the VAG signals recorded from healthy subjects and patients with knee joint disorders. The estimated densities of the VAG signals showed explicit distributions of the normal and abnormal signal groups, along with the corresponding contours in the bivariate feature space. The signal classifications were performed by using the Fisher’s linear discriminant analysis, support vector machine with polynomial kernels, and the maximal posterior probability decision criterion. The maximal posterior probability decision criterion was able to provide the total classification accuracy of 86.67% and the area (Az) of 0.9096 under the receiver operating characteristics curve, which were superior to the results obtained by either the Fisher’s linear discriminant analysis (accuracy: 81.33%, Az: 0.8564) or the support vector machine with polynomial kernels (accuracy: 81.33%, Az: 0.8533). Such results demonstrated the merits of the bivariate feature distribution estimation and the superiority of the maximal posterior probability decision criterion for analysis of knee joint VAG signals.

Highlights

The knee is composed of the femur, tibia, fibula, and patella, the extensive ligaments, and two main muscle groups [1]
For a better representation of the normal and abnormal VAG signals, we considered two waveshape features extracted in the time scale for the classification task
The normal signals recorded from the healthy subjects possess the unimodal probability density, whereas the abnormal signals associated with pathological conditions in the knee joint present more of multimodal characteristics

Summary

Introduction

The knee is composed of the femur, tibia, fibula, and patella (or knee cap), the extensive ligaments, and two main muscle groups (i.e., quadriceps and the hamstrings) [1]. The knee joint has a crescent-shaped fibrocartilaginous structure, named meniscus, which fits into the joint between the tibia and the femur. The knee joint can afford moderate stress without much internal injury, but it is not able to withstand rotational forces that commonly occur in athletic activities. In addition to the sports injury, the degeneration or damage of cartilage in the articular surface may cause rheumatic disorders such as osteoarthritis [3]. Screening of knee joint disorders at an early stage can provide preliminary information for physicians to undertake some appropriate therapy options in order to retard the degenerative process of arthritis [1]

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Results

Conclusion