Analysis of knee-joint vibroarthographic signals using bandwidth-duration localized three-channel filter bank

A vibrarthographic (VAG) signal is the recorded vibration signal produced by human knee-joints during movements of the leg. These signals are useful in quantifying lubrication and roughness of articular cartilage layers of the knee-joint. The early detection and treatment of an abnormal knee-joint will help in providing a better quality of life for the suffering patients. The VAG signals are nonlinear and non-stationary. Hence, in the proposed study, we have employed an optimal bandwidth-duration localized three-band (OBDLTB) orthogonal wavelet filter banks (OWFB) to automatically identify the health of knee-joints using VAG signals. We have used the database created at the University of Calgary containing a total of 89 VAG signals (51 normal and 38 pathological). The log-energy (LOEN) features are obtained from various sub-bands using OBDLTB-OWFB. The extracted LOEN features are separated using supervised machine learning algorithms into normal and abnormal VAG signals. Our automated system obtained an average classification accuracy of 89.89% and F1-score of 87.67% with 10-fold cross-validation. The performance of the model indicates that the proposed technique is able to identify the abnormal knee-joint reliably and thus this technique can aid the orthopedic surgeons in the early diagnosis of knee-joint abnormalities.