## 4 Vibroarthrography of the Knee Joint
### Feature Extraction and Classification
#### 4.3.1 Feature Extraction and Selection  
Feature extraction is an extremely important process in the analysis of the vibroarthographic signal following the removal of artifacts. Further processing of the filtered signal is necessary because some pathological events occur at a specific time. It involves extracting interesting features through the frequency, time, and time-frequency domains. Time domain features are characterised by their ease of acquisition and use for the description and characterisation of raw vibroarthographic signal contents. Kurtosis, skewness, and entropy may be discriminant features [123]. Kurtosis is used to measure the tendency of the probability density function (PDF) to have peaks, and skewness is used to measure the asymmetry of the PDF. Entropy is used as a representation of the nature and distribution of the PDF and to measure the inherent randomness that is observed in the probability distribution. In their work, [PERSON] et al. [123] indicate features such as the form factor (FF), which is defined by parameters of mobility, activity, and complexity. Its purpose is to provide information about signal variability. Turns count (TC) is also used to characterise VAG signals [124] and can be used to detect changes in amplitude, fluctuations, and the number of peaks [115]. [PERSON] et al. [119] identified two new features--the fractal scaling index (FSI) and averaged envelope amplitude (AEA)--to characterise the fluctuations in VAG signals. FSI was calculated using a detrended fluctuation analysis (DFA) algorithm. Measuring internal correlations and signal irregularities makes it possible to indicate pathological features in the knee joint. AEA provides information on the intensity of signal fluctuations. It is also used to measure the difference between the upper and lower envelopes of the signal over time. The Kolmogorov-Smirnov two-sampled test was used to check the statistical significance of the extracted features between the groups of pathological and normal signals. The study demonstrated a significantly increased averaged envelope amplitude of pathological signals (0.434 \(\pm\) 0.154) compared to normal signals (0.267 \(\pm\) 0.179). The fractal scaling index has an average value for the group of normal signals (0.533, StDev 0.096), while the group of pathological signals shows a significantly different distribution of fractal scaling index features (mean \(\pm\) StDev 0.635-0.092). The combination of these features allows us to distinguish healthy joints from those with pathological changes. [PERSON] et al. [11] used segmentation to isolate flexion and extension segments. Different amplitudes of acoustic emissions produced by the ascending and descending phases were recorded. The features that were extracted from the ascending phase show a difference between knees with osteoarthritis and healthy knees. Features related to the time domain were extracted by [PERSON] et al. [125] and include recurrence quantification analysis (RQA), approximate entropy (ApEn), and sample entropy (SampEn). Recurrence quantification analysis (RQA) is used to analyse complex, nonlinear dynamic systems. The key parameters used are the recurrence rate, determinism, and Shannon entropy. Approximate entropy is used to determine the levels of complexity in a time series. Sample entropy is used to reduce the bias that is caused by self-matching. SampEn is independent of the data length and has comparable consistency. Then, the wavelet energy of the suband signals was calculated based on the three features described above. Feature selection was performed to remove significant and unnecessary features, which increases the accuracy performance of the classifier. Both the genetic algorithm and the apriori algorithm were used for feature selection techniques. In their study, [PERSON] et al. [108] extracted features such as shape factor (SF), impulse factor (IF), margin factor (MF), and crest factor (CF) in addition to the previously described skewness (S) and kurtosis (K) for time domain feature extraction.