Journal
MACHINE INTELLIGENCE AND SIGNAL ANALYSIS
Volume 748, Issue -, Pages 279-290Publisher
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-981-13-0923-6_24
Keywords
Vibroarthrographic (VAG) signals; Analytic complex wavelet transform; Computer-aided diagnosis system; Support vector machine (SVM)
Ask authors/readers for more resources
Pathological conditions of knee-joints change the attributes of vibroarthrographic (VAG) signals. Abnormalities associated with knee-joints have been found to affect VAG signals. The VAG signals are the acoustic/mechanical signals captured during flexion or extension positions. The VAG feature-based methods enable a non-invasive diagnosis of abnormalities associated with knee-joint. The VAG feature-based techniques are advantageous over presently utilized arthroscopy which cannot be applied to subjects with highly deteriorated knees due to osteoarthritis, instability in ligaments, meniscectomy, or patellectomy. VAG signals are multi-component nonstationary transient signals. They can be analyzed efficiently using time-frequency methods including wavelet transforms. In this study, we propose a computer-aided diagnosis system for classification of normal and abnormal VAG signals. We have employed double density dual-tree complex wavelet transform (DDDTCWT) for sub-band decomposition of VAG signals. The L-2 norms and log energy entropy (LEE) of decomposed sub-bands have been computed which are used as the discriminating features for classifying normal and abnormal VAG signals. We have used fuzzy Sugeno classifier (FSC), least square support vector machine (LS-SVM), and sequential minimal optimization support vector machine (SMO-SVM)
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available