Abstract
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.

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














Similar content being viewed by others
References
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– 32
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):559–583
Beverland D, Kernohan G, McCoy G, Mollan R (1985) What is physiological patellofemoral crepitus? Med Biol Eng Comput 23(2):1249–1250
Bircher E (1913) Zur diagnose der meniscusluxation und des meniscusabrisses. Zentralbl f Chir 40:1852–1857
Blodgett WE (1902) Auscultation of the knee joint. The Boston Medical and Surgical Journal 146(3):63–66
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):144–152
Brooks S, Morgan M (2002) Accuracy of clinical diagnosis in knee arthroscopy. Ann R Coll Surg Engl 84 (4):265–8
Buckwalter JA, Mankin HJ (1998) Articular cartilage: degeneration and osteoarthritis, repair, regeneration, and transplantation. Instr Course Lect 47:487–504
Carl H (1885) Grundriss der chirurgie, 3rd edn. FCW Vogel , Leipzig
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 http://www.csie.ntu.edu.tw/~cjlin/libsvm
Cherkassky V, Ma Y (2004) Practical selection of SVM parameters and noise estimation for SVM regression. Neural Netw 17(1):113–126
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297
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):63–9
Dunbar M, Robertsson O, Ryd L, Lidgren L (2001) Appropriate questionnaires for knee arthroplasty. Bone & Joint Journal 83(3):339–344x
Erb KH (1933) ÜBer die möglichkeit der registrierung von gelenkgeräuschen. Deutsche Zeitschrift fü,r Chirurgie 241(11): 237–245
Fischer H, Johnson E (1961) Analysis of sounds from normal and pathologic knee joints. Arch Phys Med Rehabil 42:233
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):65–8
Guermazi A, Roemer FW, Hayashi D (2011) Imaging of osteoarthritis: update from a radiological perspective. Curr Opin Rheumatol 23(5):484–91
Hsu CW, Chang CC, Lin CJ (2010) A practical guide to support vector classification. Bioinformatics 1 (1):1–16
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):2556–61
Jackson DW, Simon TM, Aberman HM (2001) Symptomatic articular cartilage degeneration: the impact in the new millennium. Clin Orthop Relat Res 391:S14–S25
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):310–22
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):22–6
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):198–206
King G, Zeng L (2001) Logistic regression in rare events data. Polit Anal 9(2):137–163
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):773–83
Krishnan S, Rangayyan RM, Bell GD, Frank CB (2001) Auditory display of knee-joint vibration signals. J Acoust Soc Am 110(6):3292–304
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 223–228
Lin HT, Lin CJ, Weng RC (2007) A note on Platt’s probabilistic outputs for support vector machines. Mach Learn 68(3):267–276
McCauley TR, Kier R, Lynch KJ, Jokl P (1992) Chondromalacia patellae: diagnosis with MR imaging. Am J Roentgenol 158(1):101–105
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):288–93
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):13–21
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):15–23
Outerbridge RE (1961) The etiology of chondromalacia patellae. J Bone Joint Surg (Br) 43-B:752–7
Outerbridge R (1964) Further studies on the etiology of chondromalacia patellae. J Bone Joint Surg (Br) 46 (2):179–190
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):738–46
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 12:2825–2830
Peylan A (1953) Direct auscultation of the joints; preliminary clinical observations. Rheumatism 9(4):77–81
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):927–934
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 61–74
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):929–930
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):1557–68
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):23–29
Rangayyan RM, Wu YF (2008) Screening of knee-joint vibroarthrographic signals using statistical parameters and radial basis functions. Med Biol Eng Comput 46(3):223–32
Rangayyan RM, Wu Y (2009) Analysis of vibroarthrographic signals with features related to signal variability and radial-basis functions. Ann Biomed Eng 37(1):156–63
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):53–58
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 36
Sandell LJ, Aigner T (2001) Articular cartilage and changes in arthritis. An introduction: cell biology of osteoarthritis. Arthritis Res 3(2):107–13
Schindler OS (2004) Synovial plicae of the knee. Curr Orthop 18(3):210–219
Scholkopf B, Smola A, Williamson R, Bartlett P (2000) New support vector algorithms. Neural Comput 12(5):1207–45
Schölkopf B., Burges CJ (1999) Advances in kernel methods: support vector learning. MIT Press, Cambridge
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– 594
Slonim DK (2002) From patterns to pathways: gene expression data analysis comes of age. Nat Genet 32:502–508
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):959–70
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):517–23
Vapnik V (1999) An overview of statistical learning theory. IEEE Transactions on Neural Networks / a Publication of the IEEE Neural Networks Council 10(5):988–99
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):S42–9
Walters C (1929) The value of joint auscultation. The Lancet 213(5514):920–921
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):70–73
Wise CH (2015) Orthopaedic manual physical therapy from art to evidence. In: Wise CH (ed). F.A. Davis Company, Philadelphia
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):1375–1387
Wu Y (2015) Knee joint vibroarthrographic signal processing and analysis. Springer, Berlin
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 130:1–12
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):971–9
Zhang YT, Rangayyan RM, Frank CB, Bell GD (1994) Adaptive cancellation of muscle contraction interference in vibroarthrographic signals. IEEE Trans Biomed Eng 41(2):181–91
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
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.
Additional information
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.
The initial idea for this project and great support during the course of the study was provided by Dr. Jacek Czernicki, who helped with patient recruitment and with conducting the measurements. The authors would also like to thank Dr. Annie Horng for analyzing the MRI data to produce the pathological findings and further Dr. Michael Krüger-Franke for his valuable help with patient recruitment.
This work was co-funded by the German Federal Ministry for Economic Affairs and Energy under grant No. ZIM KF3177601KJ3.
Rights and permissions
About this article
Cite this article
Befrui, N., Elsner, J., Flesser, A. et al. Vibroarthrography for early detection of knee osteoarthritis using normalized frequency features. Med Biol Eng Comput 56, 1499–1514 (2018). https://doi.org/10.1007/s11517-018-1785-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11517-018-1785-4