Medical & Biological Engineering & Computing

, Volume 56, Issue 8, pp 1499–1514 | Cite as

Vibroarthrography for early detection of knee osteoarthritis using normalized frequency features

  • Nima Befrui
  • Jens ElsnerEmail author
  • Achim Flesser
  • Jacqueline Huvanandana
  • Oussama Jarrousse
  • Tuan Nam Le
  • Marcus Müller
  • Walther H. W. Schulze
  • Stefan Taing
  • Simon Weidert
Original Article


Vibroarthrography is a radiation-free and inexpensive method of assessing the condition of knee cartilage damage during extension-flexion movements. Acoustic sensors were placed on the patella and medial tibial plateau (two accelerometers) as well as on the lateral tibial plateau (a piezoelectric disk) to measure the structure-borne noise in 59 asymptomatic knees and 40 knees with osteoarthritis. After semi-automatic segmentation of the acoustic signals, frequency features were generated for the extension as well as the flexion phase. We propose simple and robust features based on relative high-frequency components. The normalized nature of these frequency features makes them insusceptible to influences on the signal gain, such as attenuation by fat tissue and variance in acoustic coupling. We analyzed their ability to serve as classification features for detection of knee osteoarthritis, including the effect of normalization and the effect of combining frequency features of all three sensors. The features permitted a distinction between asymptomatic and non-healthy knees. Using machine learning with a linear support vector machine, a classification specificity of approximately 0.8 at a sensitivity of 0.75 could be achieved. This classification performance is comparable to existing diagnostic tests and hence qualifies vibroarthrography as an additional diagnostic tool.

Graphical Abstract

Acoustic frequency features were used to detect knee osteoarthritis at 80% specificity and 75% sensitivity.


Vibroarthrography Cartilage degeneration Osteoarthritis Chondromalacia Non-invasive diagnosis 


Compliance with Ethical Standards

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.


  1. 1.
    Andersen RE, Arendt-Nielsen L, Madeleine P (2016) A review of engineering aspects of vibroarthography of the knee joint. Critical Reviews in Physical and Rehabilitation Medicine 28(1–2):13– 32Google Scholar
  2. 2.
    Ben-Dor A, Bruhn L, Friedman N, Nachman I, Schummer M, Yakhini Z (2000) Tissue classification with gene expression profiles. J Comput Biol 7(3-4):559–583PubMedCrossRefGoogle Scholar
  3. 3.
    Beverland D, Kernohan G, McCoy G, Mollan R (1985) What is physiological patellofemoral crepitus? Med Biol Eng Comput 23(2):1249–1250Google Scholar
  4. 4.
    Bircher E (1913) Zur diagnose der meniscusluxation und des meniscusabrisses. Zentralbl f Chir 40:1852–1857Google Scholar
  5. 5.
    Blodgett WE (1902) Auscultation of the knee joint. The Boston Medical and Surgical Journal 146(3):63–66CrossRefGoogle Scholar
  6. 6.
    Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. Proceedings of the fifth annual workshop on Computational learning theory COLT 92 6(8):144–152CrossRefGoogle Scholar
  7. 7.
    Brooks S, Morgan M (2002) Accuracy of clinical diagnosis in knee arthroscopy. Ann R Coll Surg Engl 84 (4):265–8PubMedPubMedCentralCrossRefGoogle Scholar
  8. 8.
    Buckwalter JA, Mankin HJ (1998) Articular cartilage: degeneration and osteoarthritis, repair, regeneration, and transplantation. Instr Course Lect 47:487–504PubMedGoogle Scholar
  9. 9.
    Carl H (1885) Grundriss der chirurgie, 3rd edn. FCW Vogel , LeipzigGoogle Scholar
  10. 10.
    Chang CC, Lin CJ (2011) LIBSVM: A library for support vector machines. ACM Trans Intell Syst Technol 2:27:1–27:27. software available at CrossRefGoogle Scholar
  11. 11.
    Cherkassky V, Ma Y (2004) Practical selection of SVM parameters and noise estimation for SVM regression. Neural Netw 17(1):113–126PubMedCrossRefGoogle Scholar
  12. 12.
    Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297Google Scholar
  13. 13.
    Dawson J, Fitzpatrick R, Murray D, Carr A (1998) Questionnaire on the perceptions of patients about total knee replacement. J Bone Joint Surg (Br) 80(1):63–9CrossRefGoogle Scholar
  14. 14.
    Dunbar M, Robertsson O, Ryd L, Lidgren L (2001) Appropriate questionnaires for knee arthroplasty. Bone & Joint Journal 83(3):339–344xGoogle Scholar
  15. 15.
    Erb KH (1933) ÜBer die möglichkeit der registrierung von gelenkgeräuschen. Deutsche Zeitschrift fü,r Chirurgie 241(11): 237–245CrossRefGoogle Scholar
  16. 16.
    Fischer H, Johnson E (1961) Analysis of sounds from normal and pathologic knee joints. Arch Phys Med Rehabil 42:233PubMedGoogle Scholar
  17. 17.
    Frank CB, Rangayyan RM, Bell GD (1990) Analysis of knee joint sound signals for non-invasive diagnosis of cartilage pathology. IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society 9(1):65–8CrossRefGoogle Scholar
  18. 18.
    Guermazi A, Roemer FW, Hayashi D (2011) Imaging of osteoarthritis: update from a radiological perspective. Curr Opin Rheumatol 23(5):484–91PubMedCrossRefGoogle Scholar
  19. 19.
    Hsu CW, Chang CC, Lin CJ (2010) A practical guide to support vector classification. Bioinformatics 1 (1):1–16Google Scholar
  20. 20.
    Hudelmaier M, Glaser C, Hohe J, Englmeier KH, Reiser M, Putz R, Eckstein F (2001) Age-related changes in the morphology and deformational behavior of knee joint cartilage. Arthritis Rheum 44(11):2556–61PubMedCrossRefGoogle Scholar
  21. 21.
    Jackson DW, Simon TM, Aberman HM (2001) Symptomatic articular cartilage degeneration: the impact in the new millennium. Clin Orthop Relat Res 391:S14–S25Google Scholar
  22. 22.
    Jackson RW, Abe I (1972) The role of arthroscopy in the management of disorders of the knee. An analysis of 200 consecutive examinations. The. J Bone Joint Surg (Br) 54(2):310–22CrossRefGoogle Scholar
  23. 23.
    Jiang CC, Liu YJ, Yip KM, Wu E (1993) Physiological patellofemoral crepitus in knee joint disorders. Bull Hosp Jt Dis (New York, N.Y.) 53(4):22–6Google Scholar
  24. 24.
    Kim KS, Seo JH, Kang JU, Song CG (2009) An enhanced algorithm for knee joint sound classification using feature extraction based on time-frequency analysis. Comput Methods Prog Biomed 94(2):198–206CrossRefGoogle Scholar
  25. 25.
    King G, Zeng L (2001) Logistic regression in rare events data. Polit Anal 9(2):137–163CrossRefGoogle Scholar
  26. 26.
    Krishnan S, Rangayyan RM, Bell GD, Frank CB (2000) Adaptive time-frequency analysis of knee joint vibroarthrographic signals for noninvasive screening of articular cartilage pathology. IEEE Trans Biomed Eng 47 (6):773–83PubMedCrossRefGoogle Scholar
  27. 27.
    Krishnan S, Rangayyan RM, Bell GD, Frank CB (2001) Auditory display of knee-joint vibration signals. J Acoust Soc Am 110(6):3292–304PubMedCrossRefGoogle Scholar
  28. 28.
    Lee TF, Lin WC, Wu LF, Wang HY (2012) Analysis of vibroarthrographic signals for knee osteoarthritis diagnosis. In: Proceedings - 2012 6th international conference on genetic and evolutionary computing, ICGEC 2012, pp 223–228Google Scholar
  29. 29.
    Lin HT, Lin CJ, Weng RC (2007) A note on Platt’s probabilistic outputs for support vector machines. Mach Learn 68(3):267–276CrossRefGoogle Scholar
  30. 30.
    McCauley TR, Kier R, Lynch KJ, Jokl P (1992) Chondromalacia patellae: diagnosis with MR imaging. Am J Roentgenol 158(1):101–105CrossRefGoogle Scholar
  31. 31.
    McCoy GF, McCrea JD, Beverland DE, Kernohan WG, Mollan RA (1987) Vibration arthrography as a diagnostic aid in diseases of the knee. A preliminary report. J Bone Joint Surg (Br) 69(2):288–93CrossRefGoogle Scholar
  32. 32.
    Menashe L, Hirko K, Losina E, Kloppenburg M, Zhang W, Li L, Hunter DJ (2012) The diagnostic performance of MRI in osteoarthritis: a systematic review and meta-analysis. Osteoarthritis and cartilage / OARS. Osteoarthritis Research Society 20(1):13–21CrossRefGoogle Scholar
  33. 33.
    Moussavi ZM, Rangayyan RM, Bell GD, Frank CB, Ladly KO, Zhang YT (1996) Screening of vibroarthrographic signals via adaptive segmentation and linear prediation modeling. IEEE Trans Biomed Eng 43 (1):15–23PubMedCrossRefGoogle Scholar
  34. 34.
    Outerbridge RE (1961) The etiology of chondromalacia patellae. J Bone Joint Surg (Br) 43-B:752–7CrossRefGoogle Scholar
  35. 35.
    Outerbridge R (1964) Further studies on the etiology of chondromalacia patellae. J Bone Joint Surg (Br) 46 (2):179–190CrossRefGoogle Scholar
  36. 36.
    Palmer AJR, Brown CP, McNally EG, Price AJ, Tracey I, Jezzard P, Carr AJ, Glyn-Jones S (2013) Non-invasive imaging of cartilage in early osteoarthritis. The bone & joint journal 95-B(6):738–46CrossRefGoogle Scholar
  37. 37.
    Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830Google Scholar
  38. 38.
    Peylan A (1953) Direct auscultation of the joints; preliminary clinical observations. Rheumatism 9(4):77–81PubMedGoogle Scholar
  39. 39.
    Pihlajamäki HK, Kuikka PI, Leppänen VV, Kiuru MJ, Mattila VM (2010) Reliability of clinical findings and magnetic resonance imaging for the diagnosis of chondromalacia patellae. JBJS 92(4):927–934CrossRefGoogle Scholar
  40. 40.
    Platt JC (1999) Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Advances in large margin classifiers. MIT Press, Cambridge, pp 61–74Google Scholar
  41. 41.
    Prior J, Mascaro B, Shark L, Stockdale J, Selfe J, Bury R, Cole P, Goodacre J (2010) Analysis of high frequency acoustic emission signals as a new approach for assessing knee osteoarthritis. Ann Rheum Dis 69(5):929–930PubMedCrossRefGoogle Scholar
  42. 42.
    Quatman CE, Hettrich CM, Schmitt LC, Spindler KP (2011) The clinical utility and diagnostic performance of magnetic resonance imaging for identification of early and advanced knee osteoarthritis: a systematic review. Am J Sports Med 39(7):1557–68PubMedPubMedCentralCrossRefGoogle Scholar
  43. 43.
    Rangayyan RM, Oloumi F, Wu Y, Cai S (2013) Fractal analysis of knee-joint vibroarthrographic signals via power spectral analysis. Biomed Signal Process Control 8(1):23–29CrossRefGoogle Scholar
  44. 44.
    Rangayyan RM, Wu YF (2008) Screening of knee-joint vibroarthrographic signals using statistical parameters and radial basis functions. Med Biol Eng Comput 46(3):223–32PubMedCrossRefGoogle Scholar
  45. 45.
    Rangayyan RM, Wu Y (2009) Analysis of vibroarthrographic signals with features related to signal variability and radial-basis functions. Ann Biomed Eng 37(1):156–63PubMedCrossRefGoogle Scholar
  46. 46.
    Rangayyan RM, Wu Y (2010) Screening of knee-joint vibroarthrographic signals using probability density functions estimated with Parzen windows. Biomed Signal Process Control 5(1):53–58CrossRefGoogle Scholar
  47. 47.
    Reed ME, Villacis DC, Hatch GFR, Burke WS, Colletti PM, Narvy SJ, Mirzayan R, Vangsness CT (2013) 3.0-tesla MRI and arthroscopy for assessment of knee articular cartilage lesions, vol 36Google Scholar
  48. 48.
    Sandell LJ, Aigner T (2001) Articular cartilage and changes in arthritis. An introduction: cell biology of osteoarthritis. Arthritis Res 3(2):107–13PubMedPubMedCentralCrossRefGoogle Scholar
  49. 49.
    Schindler OS (2004) Synovial plicae of the knee. Curr Orthop 18(3):210–219CrossRefGoogle Scholar
  50. 50.
    Scholkopf B, Smola A, Williamson R, Bartlett P (2000) New support vector algorithms. Neural Comput 12(5):1207–45PubMedCrossRefGoogle Scholar
  51. 51.
    Schölkopf B., Burges CJ (1999) Advances in kernel methods: support vector learning. MIT Press, CambridgeGoogle Scholar
  52. 52.
    Shen Y, Rangayyan RM, Bell GD, Frank CB, Zhang YT, Ladly KO (1995) Localization of knee joint cartilage pathology by multichannel vibroarthrography. Med Eng Phys 17(8):583– 594PubMedCrossRefGoogle Scholar
  53. 53.
    Slonim DK (2002) From patterns to pathways: gene expression data analysis comes of age. Nat Genet 32:502–508PubMedCrossRefGoogle Scholar
  54. 54.
    Tavathia S, Rangayyan RM, Frank CB, Bell GD, Ladly KO, Zhang YT (1992) Analysis of knee vibration signals using linear prediction. IEEE Trans Biomed Eng 39(9):959–70PubMedCrossRefGoogle Scholar
  55. 55.
    Umapathy K, Krishnan S (2006) Modified local discriminant bases algorithm and its application in analysis of human knee joint vibration signals. IEEE Trans Biomed Eng 53(3):517–23PubMedCrossRefGoogle Scholar
  56. 56.
    Vapnik V (1999) An overview of statistical learning theory. IEEE Transactions on Neural Networks / a Publication of the IEEE Neural Networks Council 10(5):988–99CrossRefGoogle Scholar
  57. 57.
    Wakefield RJ, Kong KO, Conaghan PG, Brown AK, O’Connor PJ, Emery P (2003) The role of ultrasonography and magnetic resonance imaging in early rheumatoid arthritis. Clin Exp Rheumatol 21(5 Suppl 31):S42–9PubMedGoogle Scholar
  58. 58.
    Walters C (1929) The value of joint auscultation. The Lancet 213(5514):920–921CrossRefGoogle Scholar
  59. 59.
    Welch PD (1967) The use of fast fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans Audio Electroacoust 15(2):70–73CrossRefGoogle Scholar
  60. 60.
    Wise CH (2015) Orthopaedic manual physical therapy from art to evidence. In: Wise CH (ed). F.A. Davis Company, PhiladelphiaGoogle Scholar
  61. 61.
    Wu Y, Cai S, Yang S, Zheng F, Xiang N (2013) Classification of knee joint vibration signals using bivariate feature distribution estimation and maximal posterior probability decision criterion. Entropy 15 (4):1375–1387CrossRefGoogle Scholar
  62. 62.
    Wu Y (2015) Knee joint vibroarthrographic signal processing and analysis. Springer, BerlinCrossRefGoogle Scholar
  63. 63.
    Wu Y, Chen P, Luo X, Huang H, Liao L, Yao Y, Wu M, Rangayyan RM (2016) Quantification of knee vibroarthrographic signal irregularity associated with patellofemoral joint cartilage pathology based on entropy and envelope amplitude measures. Comput Methods Prog Biomed 130:1–12CrossRefGoogle Scholar
  64. 64.
    Zhang YT, Frank CB, Rangayyan RM, Bell GD (1992) Mathematical modeling and spectrum analysis of the physiological patello-femoral pulse train produced by slow knee movement. IEEE transactions on biomedical engineeringmedical engineering 39(9):971–9CrossRefGoogle Scholar
  65. 65.
    Zhang YT, Rangayyan RM, Frank CB, Bell GD (1994) Adaptive cancellation of muscle contraction interference in vibroarthrographic signals. IEEE Trans Biomed Eng 41(2):181–91PubMedCrossRefGoogle Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2018

Authors and Affiliations

  • Nima Befrui
    • 1
  • Jens Elsner
    • 2
    Email author
  • Achim Flesser
    • 3
  • Jacqueline Huvanandana
    • 2
  • Oussama Jarrousse
    • 1
    • 2
  • Tuan Nam Le
    • 1
  • Marcus Müller
    • 2
  • Walther H. W. Schulze
    • 1
    • 2
    • 4
  • Stefan Taing
    • 2
  • Simon Weidert
    • 1
  1. 1.Trauma Surgery DepartmentUniversity Hospital of MunichMunichGermany
  2. 2.Munich Innovation LabsGrünwaldGermany
  3. 3.CPE GmbHWillichGermany
  4. 4.Evolunis UG (haftungsbeschränkt)KnesebeckGermany

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