Feature extraction of knee joint sound for non-invasive diagnosis of articular pathology

The aim of this paper is to classify the vibroarthrographic (VAG) signals according to the pathological condition using the characteristic parameters extracted by the time-frequency transform, and to evaluate the classification accuracy. VAG and knee angle signals, recorded simultaneously during one flexion and one extension of the knee, were segmented and normalized at 0.5 Hz by the dynamic time warping method. Also, the noise within the time-frequency distribution (TFD) of the segmented VAG signals was reduced by the singular value decomposition algorithm, and a back-propagation neural network (BPNN) was used to classify the normal and abnormal VAG signals. A total of 1408 segments (normal 1031, patient 377) were used for training and evaluating the BPNN. As a result, the average classification accuracy was 92.3 plusmn 0.9 %. The proposed method showed good potential for the non-invasive diagnosis and monitoring of joint disorders.

[1]  Zahra M. K. Maussavi,et al.  Screening of vibroarthrographic signals via adaptive segmentation and linear prediction modeling , 1996, IEEE Transactions on Biomedical Engineering.

[2]  H. Yoshioka,et al.  Articular cartilage of knee: normal patterns at MR imaging that mimic disease in healthy subjects and patients with osteoarthritis. , 2004, Radiology.

[3]  Rangaraj M. Rangayyan,et al.  Time-frequency signal feature extraction and screening of knee joint vibroarthrographic signals using the matching pursuit method , 1997, Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136).

[4]  I Van Breuseghem.  Ultrastructural MR imaging techniques of the knee articular cartilage: problems for routine clinical application. , 2004, European radiology.

[5]  Derrick J. Fluhme,et al.  Knee arthroscopy in the older patient , 2002 .

[6]  Rangaraj M. Rangayyan,et al.  Screening of knee-joint vibroarthrographic signals using statistical parameters and radial basis functions , 2008, Medical & Biological Engineering & Computing.

[7]  Rangaraj M. Rangayyan,et al.  Adaptive time-frequency analysis of knee joint vibroarthrographic signals for noninvasive screening of articular cartilage pathology , 2000, IEEE Transactions on Biomedical Engineering.

[8]  Ju-Hong Lee,et al.  Vibration arthrometry in the patients with failed total knee replacement , 2000, IEEE Trans. Biomed. Eng..

[9]  R M Rangayyan,et al.  Localization of knee joint cartilage pathology by multichannel vibroarthrography. , 1995, Medical engineering & physics.

[10]  H. Genant,et al.  Whole-Organ Magnetic Resonance Imaging Score (WORMS) of the knee in osteoarthritis. , 2004, Osteoarthritis and cartilage.

[11]  Hamid Hassanpour,et al.  Improved SVD-Based Technique for Enhancing the Time-Frequency Representation of Signals , 2007, 2007 IEEE International Symposium on Circuits and Systems.

[12]  Yunfeng Wu,et al.  Screening of knee-joint vibroarthrographic signals using parameters of activity and radial-basis functions , 2008, 2008 Canadian Conference on Electrical and Computer Engineering.

[13]  Karthikeyan Umapathy,et al.  Modified local discriminant bases algorithm and its application in analysis of human knee joint vibration signals , 2006, IEEE Transactions on Biomedical Engineering.

[14]  R.M. Rangayyan,et al.  Analysis of knee vibration signals using linear prediction , 1992, IEEE Transactions on Biomedical Engineering.