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Double Density Dual-Tree Complex Wavelet Transform-Based Features for Automated Screening of Knee-Joint Vibroarthrographic Signals

  • Manish Sharma
  • Pragya Sharma
  • Ram Bilas Pachori
  • Vikram M. Gadre
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 748)

Abstract

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 noninvasive 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 multicomponent 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) classifiers for the classification with tenfold cross-validation scheme. This experiment resulted in classification accuracy of 85.39%, sensitivity of 88.23%, and a specificity of 81.57%. The automated system can be used in a practical setup in the monitoring of deterioration/progress and functioning of the knee-joints. It will also help in reducing requirement of surgery for diagnosis purposes.

Keywords

Vibroarthrographic (VAG) signals Analytic complex wavelet transform Computer-aided diagnosis system Support vector machine (SVM) 

Notes

Acknowledgements

The VAG-based dataset used in this work was provided by Prof. Rangaraj M. Rangayyan, Dr. Cyril B. Frank, Dr. Gordon D. Bell, Prof. Yuan-Ting Zhang, and Prof. Sridhar Krishnan of University of Calgary, Canada. We would like to show our gratitude to them for this opportunity.

References

  1. 1.
    Wu, Y.: Knee Joint Vibroarthrographic Signal Processing and Analysis. Springer, Berlin (2015)Google Scholar
  2. 2.
    Laupattarakasem, W., Laopaiboon, M., Laupattarakasem, P., Sumananont, C.: Arthroscopic debridement for knee osteoarthritis. The Cochrane LibraryGoogle Scholar
  3. 3.
    Who department of chronic diseases and health promotion. http://www.who.int/chp/topics/rheumatic/en/. Accessed 19-08-2017
  4. 4.
    Lozano, R., Naghavi, M., Foreman, K., Lim, S., Shibuya, K., Aboyans, V., Abraham, J., Adair, T., Aggarwal, R., Ahn, S.Y., et al.: Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the global burden of disease study 2010. The Lancet 380(9859), 2095–2128 (2013)Google Scholar
  5. 5.
    U. Nations, World population to 2300, United Nations: New York, NYGoogle Scholar
  6. 6.
    Umapathy, K., Krishnan, S.: Modified local discriminant bases algorithm and its application in analysis of human knee joint vibration signals. IEEE Trans. Biomed. Eng. 53(3), 517–523 (2006)CrossRefGoogle Scholar
  7. 7.
    Rangayyan, R.M., Wu, Y.: Screening of knee-joint vibroarthrographic signals using statistical parameters and radial basis functions. Med. Biol. Eng. Comput. 46(3), 223–232 (2008)CrossRefGoogle Scholar
  8. 8.
    Rangayyan, R.M., Wu, Y.: Analysis of vibroarthrographic signals with features related to signal variability and radial-basis functions. Ann. Biomed. Eng. 37(1), 156–163 (2009)CrossRefGoogle Scholar
  9. 9.
    Sharma, M., Pachori, R.B., Acharya, U.R.: A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension. Pattern Recognit. Lett. 94, 172–179 (2017).  https://doi.org/10.1016/j.patrec.2017.03.023
  10. 10.
    Ladly, K., Frank, C., Bell, G., Zhang, Y., Rangayyan, R.: The effect of external loads and cyclic loading on normal patellofemoral joint signals. Def. Sci. J. 43(3), 201 (1993)CrossRefGoogle Scholar
  11. 11.
    Rangayyan, R.M., Krishnan, S., Bell, G.D., Frank, C.B., Ladly, K.O.: Parametric representation and screening of knee joint vibroarthrographic signals. IEEE Trans. Biomed. Eng. 44(11), 1068–1074 (1997)CrossRefGoogle Scholar
  12. 12.
    Sharma, M., Dhere, A., Pachori, R.B., Acharya, U.R.: An automatic detection of focal EEG signals using new class of time-frequency localized orthogonal wavelet filter banks. Knowl. Based Syst. 118, 217–227 (2017)CrossRefGoogle Scholar
  13. 13.
    Sharma, M., Achuth, P.V., Pachori, R.B., Gadre, V.M.: A parametrization technique to design joint time-frequency optimized discrete-time biorthogonal wavelet bases. Signal Process. 135, 107–120 (2017)CrossRefGoogle Scholar
  14. 14.
    Sharma, M., Dhere, A., Pachori, R.B., Gadre, V.M.: Optimal duration-bandwidth localized antisymmetric biorthogonal wavelet filters. Signal Process. 134, 87–99 (2017)CrossRefGoogle Scholar
  15. 15.
    Sharma, M., Bhati, D., Pillai, S., Pachori, R.B., Gadre, V.M.: Design of time-frequency localized filter banks: Transforming non-convex problem into convex via semidefinite relaxation technique. Circuits Syst. Signal Process. 35(10), 3716–3733 (2016)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Sharma, M., Gadre, V.M., Porwal, S.: An eigenfilter-based approach to the design of time-frequency localization optimized two-channel linear phase biorthogonal filter banks. Circuits Syst. Signal Process. 34(3), 931–959 (2015)CrossRefGoogle Scholar
  17. 17.
    Sharma, M., Pachori, R.B.: A novel approach to detect epileptic seizures using a combination of tunable-q wavelet transform and fractal dimension. J. Mech. Med. Biol. 1740003.  https://doi.org/10.1142/S0219519417400036. http://www.worldscientific.com/doi/pdf/10.1142/S0219519417400036
  18. 18.
    Bhati, D., Sharma, M., Pachori, R.B., Gadre, V.M.: Time-frequency localized three-band biorthogonal wavelet filter bank using semidefinite relaxation and nonlinear least squares with epileptic seizure EEG signal classification. Digit. Signal Process. 62, 259–273 (2017)CrossRefGoogle Scholar
  19. 19.
    Bhati, D., Sharma, M., Pachori, R.B., Nair, S.S., Gadre, V.M.: Design of time-frequency optimal three-band wavelet filter banks with unit sobolev regularity using frequency domain sampling. Circuits Syst. Signal Process. 35(12), 4501–4531 (2016)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Sharma, M., Kolte, R., Patwardhan, P., Gadre, V.: Time-frequency localization optimized biorthogonal wavelets. In: International Conference on Signal Processing and Communication (SPCOM), pp. 1–5 (2010)Google Scholar
  21. 21.
    Selesnick, I.W.: The double-density dual-tree dwt. IEEE Trans. Signal Process. 52(5), 1304–1314 (2004)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Neumann, J., Steidl, G.: Dual-tree complex wavelet transform in the frequency domain and an application to signal classification. Int. J. Wavelets Multiresolution Inf. Process. 3(01), 43–65 (2005)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Kingsbury, N.: A dual-tree complex wavelet transform with improved orthogonality and symmetry properties. In: Proceedings of the 2000 International Conference on Image Processing, vol. 2, pp. 375–378. IEEE (2000)Google Scholar
  24. 24.
    Sharma, M., Vanmali, A.V., Gadre, V.M.: Wavelets and Fractals in Earth System Sciences. CRC Press, Taylor and Francis Group (2013). Ch. Construction of WaveletsGoogle Scholar
  25. 25.
    Selesnick, I.W.: Hilbert transform pairs of wavelet bases. IEEE Signal Process. Lett. 8(6), 170–173 (2001)CrossRefGoogle Scholar
  26. 26.
    Sharma, M., Deb, D., Acharya, U.R.: A novel three-band orthogonal wavelet filter bank method for an automated identification of alcoholic eeg signals. Appl. Intell. (2017).  https://doi.org/10.1007/s10489-017-1042-9
  27. 27.
    Han, J., Dong, F., Xu, Y.: Entropy feature extraction on flow pattern of gas/liquid two-phase flow based on cross-section measurement. J. Phys. Conf. Ser. 147, 012041 (2009). IOP PublishingCrossRefGoogle Scholar
  28. 28.
    Sharma, A., Amarnath, M., Kankar, P.: Feature extraction and fault severity classification in ball bearings. J. Vib. Control 22(1), 176–192 (2016)CrossRefGoogle Scholar
  29. 29.
    Gupta, V., Priya, T., Yadav, A.K., Pachori, R.B., Acharya, U.R.: Automated detection of focal eeg signals using features extracted from flexible analytic wavelet transform. Pattern Recognit. Lett. 94, 180–188 (2017)Google Scholar
  30. 30.
    Kailath, T.: The divergence and Bhattacharyya distance measures in signal selection. IEEE Trans. Commun. Technol. 15(1), 52–60 (1967)CrossRefGoogle Scholar
  31. 31.
    Amo, Ad, Montero, J., Biging, G., Cutello, V.: Fuzzy classification systems. Eur. J. Oper. Res. 156(2), 495–507 (2004)Google Scholar
  32. 32.
    Ishibuchi, H., Nakaskima, T.: Improving the performance of fuzzy classifier systems for pattern classification problems with continuous attributes. IEEE Trans. Ind. Electron. 46(6), 1057–1068 (1999).  https://doi.org/10.1109/41.807986CrossRefGoogle Scholar
  33. 33.
    Suykens, J.A., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999)CrossRefGoogle Scholar
  34. 34.
    Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144–152. ACM (1992)Google Scholar
  35. 35.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge university press, Cambridge (2000)Google Scholar
  36. 36.
    Kohavi, R., et al.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Ijcai, pp. 1137–1145. Stanford, CA (1995)Google Scholar
  37. 37.
    Azar, A.T., El-Said, S.A.: Performance analysis of support vector machines classifiers in breast cancer mammography recognition. Neural Comput. Appl. 24(5), 1163–1177 (2014)CrossRefGoogle Scholar
  38. 38.
    Rangayyan, R.M., Oloumi, F., Wu, Y., Cai, S.: Fractal analysis of knee-joint vibroarthrographic signals via power spectral analysis. Biomed. Signal Process. Control 8(1), 23–29 (2013)CrossRefGoogle Scholar
  39. 39.
    Wu, Y., Chen, P., Luo, X., Huang, H., Liao, L., Yao, Y., Wu, M., Rangayyan, R.M.: Quantification of knee vibroarthrographic signal irregularity associated with patellofemoral joint cartilage pathology based on entropy and envelope amplitude measures. Comput. Methods Programs Biomed. 130, 1–12 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Manish Sharma
    • 1
  • Pragya Sharma
    • 2
  • Ram Bilas Pachori
    • 3
  • Vikram M. Gadre
    • 4
  1. 1.Department of Electrical EngineeringInstitute of Infrastructure, Technology, Research and Management (IITRAM)AhmedabadIndia
  2. 2.Department of Electronics and Communication EngineeringAcropolis Institute of Technology and ResearchIndoreIndia
  3. 3.Discipline of Electrical EngineeringIndian Institute of Technology IndoreIndoreIndia
  4. 4.Discipline of Electrical EngineeringIndian Institute of Technology BombayMumbaiIndia

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