Fractal analysis of knee-joint vibroarthrographic signals via power spectral analysis

Parameters useful for the diagnosis of pathological processes leading to the deterioration of the articular cartilage surfaces of knee joints, such as osteoarthritis, may be derived from vibroarthrographic (VAG) signals. In the present work, we explore fractal analysis to parameterize the temporal and spectral variability of normal and abnormal VAG signals. The power spectrum analysis method was used with the 1/f model to derive estimates of the fractal dimension (FD). Classification accuracy of up to 0.74 was obtained with a single FD parameter, in terms of the area under the receiver operating characteristic curve (Az), with a database of 89 VAG signals. Combinations of the features derived in the present work with other features we have reported upon recently, when used with several neural networks with radial basis functions, resulted in Az values in the range [0.92, 0.96], with an exceptional case of perfect classification with Az = 1.0. The proposed methods could help in the detection and monitoring of knee-joint pathology.