title: Parametric representation and screening of knee joint vibroarthrographic signals.
abstract: We have been investigating analysis of knee joint vibration or vibroarthrographic (VAG) signals as a potential tool for noninvasive diagnosis and monitoring of cartilage pathology. In this paper, we present a comprehensive comparative study of different parametric representations of VAG signals. Dominant poles and cepstral coefficients were derived from autoregressive models of adaptively segmented VAG signals. Signal features and a few clinical features were used as feature vectors in pattern classification experiments based on logistic regression analysis and the leave-one-out method. The results using 51 normal and 39 abnormal signals indicated the superior performance of cepstral coefficients in VAG signal classification with an accuracy rate of 75.6%. With 51 normal and 20 abnormal signals limited to chondromalacia patella, cepstral coefficients again gave the highest accuracy rate of 85.9%.
authors.affiliation: Department of Electrical and Computer Engineering, University of Calgary, Alta., Canada. ranga@enel.ucalgary.ca
authors.forename: R M
authors.initials: RM
authors.lastname: Rangayyan
authors.forename: S
authors.initials: S
authors.lastname: Krishnan
authors.forename: G D
authors.initials: GD
authors.lastname: Bell
authors.forename: C B
authors.initials: CB
authors.lastname: Frank
authors.forename: K O
authors.initials: KO
authors.lastname: Ladly
mesh_terms: D000465:Algorithms; D001175:Arthrography / Q000379:methods*; D001314:Auscultation; D006801:Humans; D007592:Joint Diseases / Q000175:diagnosis; D007718:Knee Injuries / Q000175:diagnosis*; D007719:Knee Joint / Q000503:physiopathology*; D016014:Linear Models; D016015:Logistic Models; D008991:Monitoring, Physiologic / Q000295:instrumentation / Q000379:methods; D010363:Pattern Recognition, Automated; D012016:Reference Values; D012815:Signal Processing, Computer-Assisted; D014732:Vibration