SCREENING OF VIBROARTHROGRAPHIC SIGNALS VIA ADAPTIVE SEGMENTATION ANDLINEAR PREDIATION MODELING

This paper proposes a noninvasive method to diagnose chondromalacia pa tella at its early stages by recording knee vibration signals (also kn own as vibroarthrographic or VAG signals) over the mid-patella during normal movement, An adaptive segmentation method was developed to segm ent the nonstationary VAG signals, The least squares modeling method w as used to reduce the number of data samples to a few model parameters . Model parameters along with a few clinical parameters and a signal v ariability parameter were then used as discriminant features for scree ning VAG signals by applying logistic and discriminant algorithms. The system was trained using ten normal and eight abnormal signals, It co rrectly screened a separate test set of ten normal and eight abnormal signals except for one normal signal, The proposed method should find use as an alternative technique for diagnosis of knee joint pathology or as a test before arthroscopy or major knee surgery.