Recursive least-squares lattice-based adaptive segmentation and autoregressive modeling of knee joint vibroarthrographic signals

Vibration signals emitted during movement of the knee, known as vibroarthrographic (VAG) signals, may bear diagnostic information. We propose a new technique for adaptive segmentation based on the recursive least-squares lattice algorithm to segment the non-stationary VAG signals into locally-stationary components, which were then autoregressive modeled using the Burg-Lattice method. Classification of 90 VAG signals as normal or abnormal using the signal and clinical parameters provided an accuracy of 71.1% with the leave-one-out method. When the abnormal signals were restricted to chondromalacia patella only, the classification accuracy increased to 80.3%. The results indicate that VAG is a potential tool for non-invasive screening for chondromalacia patella.