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Analysis of Vibroarthrographic Signals with Features Related to Signal Variability and Radial-Basis Functions

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Abstract

Knee-joint sounds or vibroarthrographic (VAG) signals contain diagnostic information related to the roughness, softening, breakdown, or the state of lubrication of the articular cartilage surfaces. Objective analysis of VAG signals provides features for pattern analysis, classification, and noninvasive diagnosis of knee-joint pathology of various types. We propose parameters related to signal variability for the analysis of VAG signals, including an adaptive turns count and the variance of the mean-squared value computed during extension, flexion, and a full swing cycle of the leg, for the purpose of classification as normal or abnormal, that is, screening. With a database of 89 VAG signals, screening efficiency of up to 0.8570 was achieved, in terms of the area under the receiver operating characteristics curve, using a neural network classifier based on radial-basis functions, with all of the six proposed features. Using techniques for feature selection, the turns counts for the flexion and extension parts of the VAG signals were chosen as the top two features, leading to an improved screening efficiency of 0.9174. The proposed methods could lead to objective criteria for improved selection of patients for clinical procedures and reduce healthcare costs.

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References

  1. Challis R. E., Kitney R. I. (1990) Biomedical signal processing (in four parts): Part 1. Time-domain methods. Med. Biol. Eng. Comput. 28:509–524

    Article  PubMed  CAS  Google Scholar 

  2. Chen S., Cowan C. F. N., Grant P. M. (1991) Orthogonal least squares learning algorithm for radial basis function networks. IEEE Transactions on Neural Networks 2(2):302–309

    Article  PubMed  CAS  Google Scholar 

  3. Chu M. L., Gradisar I. A., Zavodney L. D. (1978) Possible clinical application of a noninvasive monitoring technique of cartilage damage in pathological knee joints. Journal of Clinical Engineering 3(1):19–27

    Google Scholar 

  4. Duda R. O., Hart P. E. (1973) Pattern Classification and Scene Analysis. Wiley, New York, NY

    Google Scholar 

  5. Frank C. B., Rangayyan R. M., Bell G. D. (1990) Analysis of knee sound signals for non-invasive diagnosis of cartilage pathology. IEEE Engineering in Medicine and Biology Magazine 9(1):65–68

    Article  PubMed  CAS  Google Scholar 

  6. Haykin S. (2002) Neural Networks: A Comprehensive Foundation 2nd edition. Prentice-Hall PTR, Englewood Cliffs, NJ

    Google Scholar 

  7. Hjorth B. (1970) EEG analysis based on time domain properties. Electroencephalography and Clinical Neurophysiology 29:306–310

    Article  PubMed  CAS  Google Scholar 

  8. Hjorth B. (1973) The physical significance of time domain descriptors in EEG analysis. Electroencephalography and Clinical Neurophysiology 34:321–325

    Article  PubMed  CAS  Google Scholar 

  9. Hjorth, B. Time domain descriptors and their relation to a particular model for generation of EEG activity. In: CEAN: Computerised EEG Analysis, edited by G. Dolce and H. Künkel. Stuttgart, Germany: Gustav Fischer, 1975, pp. 3–8.

  10. Jiang C. -C., Lee J. -H., Yuan T. -T. (2000) Vibration arthrometry in the patients with failed total knee replacement. IEEE Transactions on Biomedical Engineering 47(2):218–227

    Google Scholar 

  11. Kendall M. (1976) Time-Series 2nd edition. Charles Griffin, London, UK

    Google Scholar 

  12. Krishnan S., Rangayyan R. M., Bell G. D., Frank C. B. (2000) Adaptive time-frequency analysis of knee joint vibroarthrographic signals for noninvasive screening of articular cartilage pathology. IEEE Transactions on Biomedical Engineering 47(6):773–783

    Article  PubMed  CAS  Google Scholar 

  13. Krishnan S., Rangayyan R. M., Bell G. D., Frank C. B., Ladly K. O. (1997) Adaptive filtering, modelling, and classification of knee joint vibroarthrographic signals for non-invasive diagnosis of articular cartilage pathology. Medical & Biological Engineering & Computing 35:677–684

    Article  PubMed  CAS  Google Scholar 

  14. Ladly K. O., Frank C. B., Bell G. D., Zhang Y. T., Rangayyan R. M. (1993) The effect of external loads and cyclic loading on normal patellofemoral joint signals. Special Issue on Biomedical Engineering, Defence Science Journal (India) 43:201–210

    Google Scholar 

  15. Lund F., Nilsson B. E. (1980) Arthroscopy of the patello-femoral joint. Acta Orthopaedica Scandinavica 51:297–302

    Article  PubMed  CAS  Google Scholar 

  16. McCoy G. F., McCrea J. D., Beverland D. E., Kernohan W. G., Mollan R.A.B. (1987) Vibration arthrography as a diagnostic aid in diseases of the knee. The Journal of Bone and Joint Surgery 69-B(2):288–293

    Google Scholar 

  17. Metz C. E. (1978) Basic principles of ROC analysis. Seminars in Nuclear Medicine VIII(4):283–298

    Article  Google Scholar 

  18. Metz, C. E., and L. Pesce. Readings in ROC Analysis, with Emphasis on Medical Applications. Available at www.radiology.uchicago.edu/krl/KRL_ROC/ROC_analysis_by_topic4.htm. Chicago, IL: University of Chicago, 2006.

  19. Moussavi Z. M. K., Rangayyan R. M., Bell G. D., Frank C. B., Ladly K. O., Zhang Y. T. (1996) Screening of vibroarthrographic signals via adaptive segmentation and linear prediction modeling. IEEE Transactions on Biomedical Engineering 43(1):15–23

    Article  PubMed  CAS  Google Scholar 

  20. Mu, T., A. K. Nandi, and R. M. Rangayyan. Strict 2-surface proximal classification of knee-joint vibroarthrographic signals. In: Proc. 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Lyon, France: IEEE, pp. 4911–4914, August 2007

  21. Rangayyan R. M. (2002) Biomedical Signal Analysis—A Case-Study Approach. IEEE and Wiley, New York, NY

    Google Scholar 

  22. Rangayyan R. M., Krishnan S., Bell G. D., Frank C. B., Ladly K. O. (1997) Parametric representation and screening of knee joint vibroarthrographic signals. IEEE Transactions on Biomedical Engineering 44(11):1068–1074

    Article  PubMed  CAS  Google Scholar 

  23. Rangayyan R. M., Wu Y. F. (2008) Screening of knee-joint vibroarthrographic signals using statistical parameters and radial basis functions. Medical & Biological Engineering & Computing 46(3):223–232

    Article  PubMed  Google Scholar 

  24. Rangayyan, R. M., and Y. F. Wu. Screening of knee-joint vibroarthrographic signals using parameters of activity and radial-basis functions. In: Proc. 21st IEEE Canadian Conference on Electrical and Computer Engineering. Niagara Falls, Ontario, Canada: IEEE, pp. 57–60, May 2008

  25. Reddy N. P., Rothschild B. M., Mandal M., Gupta V., Suryanarayanan S. (1995) Noninvasive acceleration measurements to characterize knee arthritis and chondromalacia. Annals of Biomedical Engineering 23:78–84

    Article  PubMed  CAS  Google Scholar 

  26. Reddy N. P., Rothschild B. M., Verrall E., Joshi A. (2001) Noninvasive measurement of acceleration at the knee joint in patients with rheumatoid arthritis and spondyloarthropathy of the knee. Annals of Biomedical Engineering 29(12):1106–1111

    Article  PubMed  CAS  Google Scholar 

  27. Umapathy K., Krishnan S. (2006) Modified local discriminant bases algorithm and its application in analysis of human knee joint vibration signals. IEEE Transactions on Biomedical Engineering 53(3):517–523

    Article  PubMed  Google Scholar 

  28. Ware J. H., Mosteller F., Delgado F., Donnelly C., Ingelfinger J. A. (1992) P values. In: Bailar J.C. III, Mosteller F. (eds) Medical Uses of Statistics, second edition. NEJM Books, Boston, MA, pp. 181–200

    Google Scholar 

  29. Willison R. G. (1964) Analysis of electrical activity in healthy and dystrophic muscle in man. Journal of Neurology, Neurosurgery, and Psychiatry 27:386–394

    Article  PubMed  CAS  Google Scholar 

Download references

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. R. M. Rangayyan is a Visiting Professor at the Beijing University of Posts and Telecommunications. We thank Yachao Zhou and Xiaoxuan Zhu for technical assistance and review of this paper.

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Authors and Affiliations

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

    Rangaraj M. Rangayyan

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

    Yunfeng Wu

Authors
  1. Rangaraj M. Rangayyan
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  2. Yunfeng Wu
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Correspondence to Rangaraj M. Rangayyan.

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Rangayyan, R.M., Wu, Y. Analysis of Vibroarthrographic Signals with Features Related to Signal Variability and Radial-Basis Functions. Ann Biomed Eng 37, 156–163 (2009). https://doi.org/10.1007/s10439-008-9601-1

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  • DOI: https://doi.org/10.1007/s10439-008-9601-1

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