Pathological conditions of knee joints have been observed to cause changes in the characteristics of vibroarthrographic (VAG) signals. Several studies have proposed many parameters for the analysis and classification of VAG signals; however, no statistical modeling methods have been explored to analyze the distinctions in the probability density functions (PDFs) between normal and abnormal VAG signals. In the present work, models of PDFs were derived using the Parzen-window approach to represent the statistical characteristics of normal and abnormal VAG signals. The Kullback-Leibler distance was computed between the PDF of the signal to be classified and the PDF models for normal and abnormal VAG signals. Additional statistical measures, including the mean, standard deviation, coefficient of variation, skewness, kurtosis, and entropy, were also derived from the PDFs obtained. An overall classification accuracy of 77.53%, sensitivity of 71.05%, and specificity of 82.35% were obtained with a database of 89 VAG signals using a neural network with radial basis functions with the leave-one-out procedure for cross validation. The screening efficiency was derived to be 0.8322, in terms of the area under the receiver operating characteristics curve. (C) 2009 Elsevier Ltd. All rights reserved.
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