Analysis and multiclass classification of pathological knee joints using vibroarthrographic signals
Introduction
Vibroarthrography (VAG) is an experimental method developed for noninvasive assessment of articular function, especially arthrokinematics. The VAG method is based on the analysis of high frequency vibroacoustic emission, which is a natural phenomenon acquired from the relative motion of articular surfaces of the synovial joint (diarthrosis) [1], [2], [3]. In physiological conditions, articular surfaces covered by hyaline cartilage are smooth and slippery, which determines optimal arthrokinematic motion quality [1], [4]. In contrast, degenerated cartilage results in greater friction during movement, which is reflected in an increase in amplitude and frequency of the VAG signal [2]. Chondral lesions (such as chondromalacia or osteoarthritis) are often observed in a patellofemoral joint (PFJ), a part of the knee joint complex, which can be explained by its specific biomechanical environment and substantial involvement in daily/sports activity. Due to this but also due to having the greatest susceptibility to the VAG test resulting from a superficial position, the knee is the joint most commonly analyzed by VAG.
Although the VAG method is still in development, it shows high accuracy, sensitivity and specificity, when comparing results obtained from controls and a non-specific, knee-related disorder group [5]. Nalband et al. [6] applied the least square support vector machines algorithm based on the time-complexity parameters of the VAG signal and obtained greater than 94% classification accuracy, greater than 98% sensitivity and 86% specificity [6]. Kim et al. [4] presented classification of the neural network with frequency parameters as inputs, which allowed for improvement of the accuracy to more than 95%, sensitivity 92% and specificity of more than 98% [4]. The best results of the normal-abnormal classification signal are found in the work of Rangayyan et al. [7]. The authors used a classifier based on a radial basis function network with statistical parameters in the time domain. Here, the accuracy, sensitivity and specificity reached 100%, with the cross-validation of the leave-one-out method [7].
Nevertheless, it should be noted that the mentioned authors based their analysis only on two classes, normal and abnormal. However, from a clinical point of view, the 2-class classification is insufficient for an appropriate diagnostic process and further adequate treatment, and a more specific categorization of chondral-related changes is necessary [8]. Similarly, the radiological staging of chondral disorders (especially early stages, such as chondromalacia) using X-ray also possess significant limitations, due to the low sensitivity and specificity [9]. On the other hand, availability of modern imaging methods such as magnetic resonance imaging (MRI) is limited due to the high expense [9]. Moreover, current diagnostic methods are entirely observer-dependent and require significant knowledge, expertise, and time. Therefore, due to the constantly increasing incidence of age-related cartilage lesions, there have been calls for the development of noninvasive, observer-independent and financially accessible methods for evaluation of human joints, with sensitivity and specificity comparable with MRI, considered the gold standard for chondral lesion assessment.
Recently it has been demonstrated that the VAG method could be helpful in differentiating particular disorders of the PFJ and its stages, due to the specific, disorder-related character of the VAG signal pattern [2], [9]. However, while the problem of classification of normal and abnormal VAG signals has been studied, extending it to a multiclass classification remains practically unaddressed. Moreover, as previously suggested, further work is needed to determine whether the sensitivity and specificity of the VAG method are sufficient for clinical application [10]. Furthermore, there is a pressing need for description of optimal algorithms for VAG signal multiclass classification, in accordance with the clinical criteria of PFJ chondral lesions [Nalband]. Optimization of diagnostic methods should include the selection of the most relevant and discriminating VAG signal parameters, followed by selection of an optimal predictive model [11], [12], [13], [14]. This will allow us to develop an observer-independent, sensitive, computer-aided diagnostic method, useful for clinicians, in particular for orthopedists and physiotherapists, who are concerned with evaluation of the quality of arthrokinematic motion during physical examination [1].
Thus, the primary goal of our study will be to extend the VAG signal categorization of various PFJ chondral lesions to a 5-class classification (normal and four classes of disorders). Our analyses will be performed with respect to the MRI examination, as a reference method of noninvasive assessment of chondral lesions, which will allow us to evaluate the sensitivity and specificity of the VAG method. For the optimization problem, we applied two algorithms for selecting the best parameters: genetic search and selection based on simple regression functions. Then we compared four classification models representing different approaches to the classification problem: logistic regression with automatic attribute selection based on simple regression functions, multilayer perceptron with sigmoid activation function, sequential minimal optimization algorithm implementation of support vector machine classifier and random forest tree.
The paper is structured as follows: Section 2 describes the analyzed material and the methodology of the study, including the feature extraction techniques, feature selection algorithms and classification methods. Section 3 presents the obtained results, which are discussed in Section 4. Finally, Section 5 presents our conclusions.
Section snippets
Participants
121 knees from 56 patients with chondromalacia patellae (CMP) (38 with bilateral and 20 with unilateral symptoms) and 16 patients with osteoarthritis (OA) (10 with bilateral and 6 with unilateral symptoms) were enrolled in the study. Knees with CMP were classified into 3 grades according to criteria of the International Cartilage Repair Society by MRI imaging: CMP stage I (28 knees; CMPI), CMP stage II (31 knees; CMPII) and CMP stage III (36 knees; CMPIII) [2], [9]. OA patients were selected
Results
In this section we describe the results of VAG signal parameterization and selection of the most relevant discriminate parameters and models using software and algorithms which are described in detail in the previous section.
Discussion
Results presented in this paper confirm the high usefulness of the quantitative analysis of VAG signals based on classification techniques for normal and pathological knees [4], [5], [6], [26]. We obtained over 90% levels of accuracy and AUC metrics, more than 93% sensitivity and more than 84% specificity for the logistic regression-based method (SimpleLogistic). These values are slightly lower than those we can find in the published research works, where the values obtained for accuracy,
Conclusion
In conclusion, we found that the VAG method seems to be sensitive enough for evaluating the biomechanical and morphological changes within the knee joint environment. Analysis and the 5-class classification of the VAG signals give satisfactory results not only for the screening tests but also for classifying signals of various knee joint disorders and their stages, according to the level of chondral lesion [33], [34]. Thus, the VAG method may constitute a helpful tool for clinicians and may be
Acknowledgments
We would like to thank all of the subjects who participated in the study.
The project was approved by the Ethics Committee of Opole Voivodship. Signed informed consent was obtained from all tested persons.
The authors declare no conflict of interest.
The authors report no external funding source for this study.
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