# A Review of the Vibration Arthography Technique Applied to the Knee Diagnostics
## 3 Signal Analysis
### Classification  
Classification is the last step in the post-processing method described above. Its aim is to predict a pathology from measured signals. Typically, the classification model is trained with two datasets. One belongs to the normal and the other to abnormal knees.  
A range of the parameters and statistics discussed in Section 3.2 may be used for classification. Nevertheless, the parameter selection is essential and it should include highly discriminant features to increase the predictive accuracy [2]. So-called supervised classification has therefore been used recently, and relies on chosen features that will affect the presence or absence of knee pathology. [PERSON] et al. [27] performed a supervised classification to distinguish asymptomatic versus OA knees, thanks to a Linear Support Vector Machine. Their results were conclusive, as the specificity and sensitivity reached 80% and 75%, respectively. This accuracy is as high as that of existing diagnostic tests.  
Figure 6: (**A**) A typical signal encountered in VAG data is a short-duration “click”. Here, it is measured by three accelerometers and is associated with the posterior horn tear of the meniscus by [PERSON] [7]. (**B**) Typical wavelet forms are used in the post-processing of VAG signals. Daubechies wavelet was used because of its resemblance with short-duration pulses of the VAG signal.  
Similarly, [PERSON] et al. classified with an average accuracy of 91.4% normal and abnormal VAG signals from four parameters that are mentioned above in the time-frequency analysis section [16]. Furthermore, normal and OA knees have been classified using a discriminant function based on four other parameters [35]. Classification results showed a sensitivity of 89.52%, a specificity of 67.50%, and a total accuracy rate of 81.52%. Moreover, [PERSON] et al. [36] used the Least-Squares Support Vector Machine (LS-SVM) and the Bayesian Decision Rule (BDR) to classify knee joint vibroarthographic signals. The VAG signal is represented by a two-dimensional feature vector composed of a fractal scaling index and the average envelope amplitude. Figure 7 illustrates the classification result of the two techniques. It can be observed that the least-square support vector machine and the Bayesian decision rule provide two different nonlinear decision boundaries and produce overall accuracy rates of 82.67% and 88%, respectively. Other classification studies are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors of Research Study** & **Population** & \begin{tabular}{c} **Processing Technique:** \\ **Extracted Features** \\ \end{tabular} & \
\begin{tabular}{c} **Aim of the Study and** \\ **Results** \\ \end{tabular} \\ \hline \multirow{3}{*}{[PERSON] et al. (2001) [19]} & \multirow{3}{*}{
\begin{tabular}{c} 11 spondyloarthritis--11 rheumatoid arthritis knees \\ \end{tabular} } & FD analysis: (Discrete Fourier Transform): Mean power of the power spectrum in the frequency range of 100–500 Hz & Differentiate spondyl and rheumatoid arthritis signals \\ \hline \multirow{3}{*}{[PERSON] et al. (2008) [22]} & 51 H—38 with knee joint pathology & TD analysis: FF (extension/flexion), S, K, E & Classification of normal and abnormal knees \\ \hline \multirow{3}{*}{[PERSON] et al. (2009) [23]} & 51 H—38 with knee joint pathology & TD analysis: TC, VMS & Classification of normal and abnormal knees \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: A sample of the literature available on the use of the vibration arthrography technique on knee joints as a classification end.  
Figure 7: Decision boundaries provided by the least-square support vector machine (LS-SVM) with polynomial kernels and the Bayesian decision rule applied on VAG signals [36].