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Screening of knee-joint vibroarthrographic signals using statistical parameters and radial basis functions

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Abstract

Externally detected vibroarthrographic (VAG) signals bear diagnostic information related to the roughness, softening, breakdown, or the state of lubrication of the articular cartilage surfaces of the knee joint. Analysis of VAG signals could provide quantitative indices for noninvasive diagnosis of articular cartilage breakdown and staging of osteoarthritis. We propose the use of statistical parameters of VAG signals, including the form factor involving the variance of the signal and its derivatives, skewness, kurtosis, and entropy, to classify VAG signals as normal or abnormal. With a database of 89 VAG signals, screening efficiency of up to 0.82 was achieved, in terms of the area under the receiver operating characteristics curve, using a neural network classifier based on radial basis functions.

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Acknowledgments

This work was supported by the Doctoral Program Foundation of the Ministry of Education of China under Grant No. 20060013007 awarded to Y. F. Wu, and by the University of Calgary in the form of a “University Professorship” awarded to R. M. Rangayyan. We thank Dr. Cyril B. Frank and Dr. G. Douglas Bell, Department of Surgery and Sport Medicine Centre, University of Calgary, for their contributions to previous related projects.

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  1. Department of Electrical and Computer Engineering Schulich School of Engineering, 2500 University Dr. NW University of Calgary, Calgary, AB, Canada, T2N 1N4

    Rangaraj M. Rangayyan

  2. School of Information Engineering, Beijing University of Posts and Telecommunications, 10 Xi Tu Cheng Road, Haidian District, Beijing, 100876, China

    Y. F. Wu

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Correspondence to Rangaraj M. Rangayyan.

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Rangayyan, R.M., Wu, Y.F. Screening of knee-joint vibroarthrographic signals using statistical parameters and radial basis functions. Med Biol Eng Comput 46, 223–232 (2008). https://doi.org/10.1007/s11517-007-0278-7

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