Screening of knee joint vibroarthrographic signals by statistical pattern analysis of dominant poles

Abstract

Analysis of human knee joint vibration signals or vibroarthrographic (VAG) signals could lead to a noninvasive method for the diagnosis of cartilage pathology. In this study, the nonstationary VAG signals were adaptively segmented into locally stationary segments. Autoregressive (AR) model coefficients were derived from the stationary segments by using the Burg-lattice method. The dominant poles of the models extracted from the AR polynomials and a signal variability parameter were used as VAG signal features. The VAG signal features with a few relevant clinical parameters were used as feature vectors in statistical pattern classification experiments based on logistic regression analysis. The results indicated a classification accuracy of 81.7% in screening 90 VAG signals with no restriction imposed on the type of abnormal signals, and an accuracy of 93.7% in classifying 71 VAG signals with abnormal signals restricted to a specific type of articular cartilage pathology known as chondromalacia patella.