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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.
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