Abstract:
The vibration and sound pattern generated by knee joints during their movement reflects their
condition. The pattern may vary due to any injury, damage, or degeneration. For further clinical
treatment, it is important to diagnose early any knee joint disorders. Several studies based on
time-frequency parameters and statistical modelling techniques have been proposed for
analyzing and classifying VAG signals, but no prominent work has been done in this regard by
using Deep learning techniques. In the analyses of medical images, deep learning algorithms
are proved to be very successful due to which Convolutional Neural Networks (CNNs) have
gained much attention. In the research work that is being presented here, a new Deep Learning
based method combined with supervised Machine learning pattern classification algorithms is
presented for analyzing and classifying VAG signals. The time-frequency analysis of VAG
signals is performed via Short-time Fourier transform (STFT) and that time-frequency
distribution is considered as a 2D image of time-frequency. We have also proposed to use pretrained Convolutional Neural Network (CNN) models for feature extraction. Further, Principal
Component Analysis (PCA) algorithm is employed for the selection of significant features, for
reducing the dimensions of feature vector and to increase interpretability. Support vector
machine (SVM) and Random Forest both were used for classification of healthy and
pathological subjects. Statistical results obtained concludes that the proposed method can
classify VAG signals with a classification accuracy of 92.13%, specificity of 92.16%,
sensitivity of 92.11%, and a Matthews Correlation Coefficient (MCC) score of 0.84. The
proposed method can accurately screen around 98% of subjects based on area under the ROC
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