pp 1-11 | Cite as

Estimation of Posturographic Trajectory Using k-Nearest Neighbors Classifier in Patients with Rheumatoid Arthritis and Osteoarthritis

  • Beata Sokołowska
  • Teresa Sadura-Sieklucka
  • Leszek Czerwosz
  • Marta Hallay-Suszek
  • Bogdan Lesyng
  • Krystyna Księżopolska-Orłowska
Chapter
Part of the Advances in Experimental Medicine and Biology book series

Abstract

Rheumatoid arthritis (RA) and osteoarthritis (OA) are common rheumatic diseases and account for a significant percentage of disability. Posturography is a method that assesses postural stability and quantitatively evaluates postural sways. The objective of this study was to estimate posturographic trajectories applying pattern recognition algorithms. To this end, k-nearest neighbors (k-NN) classifier was used to differentiate between healthy subjects and patients with OA and RA. The following parameters of trajectories were computed: radius of sways, developed area, total length, and two directional components of sways: length of left-right and forward-backward motions. Posturographic tests were applied with eyes open and closed, and with biofeedback control. We found that in RA, the radius of sways, the trajectory area, and the biofeedback coordination were related to the patients’ condition. The trajectory dynamics in OA patients were smaller compared to those in RA patients. The smallest misclassification errors were observed after feature selection in the biofeedback test compared with the eyes open and closed tests. We conclude that the estimation of posturographic trajectory with k-NN classifier could be helpful in monitoring the condition of RA patients.

Keywords

Body balance k-NN classifier Osteoarthritis Pattern recognition Postural stability Posturography Rheumatoid arthritis 

Notes

Acknowledgments

We thank Dr. A. Jóźwik for making his k-NN software available for this study and Dr. F. Rakowski for valuable remarks concerning the posturographic trajectories. The work was supported by grant MMRC PAS and the Faculty of Physics of Warsaw University (grant BST-1733000/bf task 34).

Conflicts of Interest

The authors declare no conflicts of interest in relation to this article.

References

  1. Aletaha D, Neogi T, Silman AJ, Funovis J, Felson DT, Bingham COIII et al (2010) Rheumatoid arthritis classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative. Ann Rheum Dis 69:1580–1588CrossRefPubMedGoogle Scholar
  2. Answer S, Alghadir A, Brismee JM (2015) Effect of home exercise program in patients with knee osteoarthritis: a systematic review and meta-analysis. J Geriatr Phys Ther 39(1):38–48CrossRefGoogle Scholar
  3. Arpaia P, Cimmino P, De Matteis E, D’Addio G (2014) A low-cost force sensor-based posturographic plate for home care telerehabilitation exergaming. Measurement 51:400–410CrossRefGoogle Scholar
  4. Baratto L, Morasso PG, Re C, Spada G (2002) A new look at posturographic analysis in the clinical context: sway-density vs. other parametrization techniques. Motor Contr 6:246–270CrossRefGoogle Scholar
  5. Bingham PM, Calhoun B (2015) Digital posturography games correlate with gross motor function in children with cerebral palsy. Games Health J 4(2):1–4CrossRefGoogle Scholar
  6. Błaszczyk JW (2016) The use of force-plate posturography in the assessment of postural instability. Gait Posture 44:1–6CrossRefPubMedGoogle Scholar
  7. Błaszczyk JW, Beck M, Sadowska D (2014) Assessment of postural stability in young healthy subjects based on directional features of posturographic data: vision and gender effects. Acta Neurobiol Exp 74(4):433–442Google Scholar
  8. Brenton-Rule A, D’Almeida S, Basset S, Carroll M, Dalbeth N, Rome K (2014) The effects of sandals on postural stability in patients with rheumatoid arthritis: an exploratory study. Clin Biomech 29:350–353CrossRefGoogle Scholar
  9. Chaudhry H, Bukiet B, Ji Z, Findley T (2011) Measurement of balance in computer posturography: comparison of methods – a brief review. J Bodyw Mov Ther 15(1):82–91CrossRefPubMedGoogle Scholar
  10. Cretual A (2015) Which biomechanical models are currently used in standing posture analysis? Neurophysiol Clin 45:285–295CrossRefPubMedGoogle Scholar
  11. Czerwosz L, Szczepek E, Sokołowska B, Jurkiewicz J, Czernicki Z (2013) Posturography in differential diagnosis of normal pressure hydrocephalus and brain atrophy. Adv Exp Med Biol 755:311–324CrossRefPubMedGoogle Scholar
  12. Duda OR, Hart PE, Stork DG (2000) Pattern classifcation. Wiley Interscience, New YorkGoogle Scholar
  13. Duque G, Boersma D, Loza-Diaz G, Hassan S, Suarez H, Geisinger D, Suriyaarachchi P, Sharma A, Demontiero O (2013) Effects of balance training using a virtual-reality system in older fallers. Clin Interv Aging 8:257–263CrossRefPubMedPubMedCentralGoogle Scholar
  14. Fix E, Hodges JL (1952) Discriminatory analysis: nonparametric discrimination small sample performance. Project 21-49-004, Report Number 11, USAF School of Aviation Medicine, Randolph Field, TexasGoogle Scholar
  15. Gibofsky A (2012) Overview of epidemiology, pathophysiology, and diagnosis of rheumatoid arthritis. Am J Manag Care 18:S295–S302PubMedGoogle Scholar
  16. Glyn-Jones S, Palmera AJ, Agricola R, Price AJ, Vincent TL, Carr AJ (2015) Osteoarthritis. Lancet 386(9991):376–387CrossRefPubMedGoogle Scholar
  17. Guidelines (2002) Management of rheumatoid arthritis -2002 update. Arthritis Rheum 46(2):328–346CrossRefGoogle Scholar
  18. Hen SS, Geurts AC, van’t Pad BP, Laan RF, Mulder T (2000) Postural control in rheumatoid arthritis patients scheduled for total knee arthroplasty. Arch Phys Med Rehabil 81:1489–1493CrossRefGoogle Scholar
  19. Jain AK (2000) Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 22(1):4–37CrossRefGoogle Scholar
  20. Johanson VL, Hunter DJ (2014) The epidemiology of osteoarthritis. Best Pract Res Clin Rheumatol 28:5–15CrossRefGoogle Scholar
  21. Jóźwik A, Sokołowska B, Niebroj-Dobosz I, Janik P, Kwieciński H (2011) Extraction of biomedical traits for patients with amyotrophic lateral sclerosis using parallel and hierarchical classifiers. Int J Biometrics 3(1):85–94CrossRefGoogle Scholar
  22. Kim HS, Yun DH, Yoo SD, Kim DH, Jeong YS, Yun JS, Hwang DG, Jung PK, Choi SH (2011) Balance control and knee osteoarthritis severity. Ann Rehabil Med 35:701–709CrossRefPubMedPubMedCentralGoogle Scholar
  23. Llorens R, Colomer-Font C, Alcaniz M, Noe-Sebastian E (2013) BioTrak virtual reality system: effectiveness and satisfaction analysis for balance rehabilitation in patients with brain injury. Neurologia 28(5):268–275CrossRefPubMedGoogle Scholar
  24. Maciejewska A, Jóźwik A, Kuśmierek JT, Sokołowska B (2008) Application of the k-NN classifier for mutagenesis test. Recognition of the wild type and defective in DNA repair bacterial strains on the basis of adaptive response to alkylating agents. Biocybern Biomed Eng 28(3):45–50Google Scholar
  25. Negahban H, Sanjari MA, Karimi M, Parnianpour M (2016) Complexity and variability of the center of pressure time series during quiet standing in patients with knee osteoarthritis. Clin Biomech 32:280–285CrossRefGoogle Scholar
  26. Paillard T, Noé F (2015) Techniques and methods for testing the postural function in healthy and pathological subjects. Biomed Res Int 2015:891390PubMedPubMedCentralGoogle Scholar
  27. Park HJ, Ko S, Hong HM, Ok E, Lee JI (2013) Factors related to standing balance in patients with knee osteoarthritis. Ann Rehabil Med 37(3):373–378CrossRefPubMedPubMedCentralGoogle Scholar
  28. Park EC, Kim SG, Lee CW (2015) The effects of virtual reality game exercise on balance and gait of the elderly. J Phys Ther Sci 27:1157–1159CrossRefPubMedPubMedCentralGoogle Scholar
  29. Piirtola M, Era P (2006) Force platform measurements as predictors of falls among older people – a review. Gerontology 52(1):1–16CrossRefPubMedGoogle Scholar
  30. Sangha O (2000) Epidemiology of rheumatic diseases. Rheumatology 39(Suppl 2):3–12ADSCrossRefPubMedGoogle Scholar
  31. Scott DL, Wolfe F, Huizinga TW (2010) Rheumatoid arthritis. Lancet 376(9746):1094–1098CrossRefPubMedGoogle Scholar
  32. Sokołowska B, Jóźwik A, Pokorski M (2003) A fuzzy-classifier system to distinguish respiratory patterns evolving after diaphragm paralysis in the cat. Jpn J Physiol 53(4):301–307CrossRefPubMedGoogle Scholar
  33. Sokolowska B, Jozwik A, Niebroj-Dobosz I, Janik P, Kwiecinski H (2009) Evaluation of matrix metalloproteinases in serum of patients with amyotrophic lateral sclerosis with pattern recognition methods. J Physiol Pharmacol 60(Suppl 5):117–120PubMedGoogle Scholar
  34. Sokołowska B, Jóźwik A, Niebroj-Dobosz I, Hausmanowa-Petrusewicz I (2014) A pattern recognition approach to Emery-Dreifuss muscular dystrophy (EDMD) study. MIT J 23:165–171Google Scholar
  35. Sokołowska B, Czerwosz L, Hallay-Suszek M, Sadura-Sieklucka T, Księżopolska-Orłowska K (2015) Posturography in patients with rheumatoid arthritis and osteoarthritis. Adv Exp Med Biol 2:63–70Google Scholar
  36. Visser JE, Carpenter MG, van de Kooij H, Bloem BR (2008) The clinical utility of posturography. Clin Neurophysiol 119(11):2424–2436CrossRefPubMedGoogle Scholar
  37. Westwood OM, Nelson PN, Hay FC (2006) Rheumatoid factors: what’s new? Rheumatology (Oxford) 45(4):379–385CrossRefGoogle Scholar
  38. Wong R, Davis AM, Badley E, Grewal R, Mohammed M (2010) Prevalence of arthritis and rheumatic diseases around the world. A growing burden and implications for health care needs. Arthritis community research and evaluation unit. http://www.acreu.ca/moca. Accessed 20 Oct 2017
  39. Zhang Z, Lion A, Chary-Valckenaere I, Loeuille D, Rat A-K, Paysant J, Perrin PP (2015) Diurnal variation on balance control in patients with symptomatic knee osteoarthritis. Arch Gerontol Geriatr 61(1):109–114CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing AG  2018

Authors and Affiliations

  • Beata Sokołowska
    • 1
  • Teresa Sadura-Sieklucka
    • 2
  • Leszek Czerwosz
    • 1
  • Marta Hallay-Suszek
    • 3
  • Bogdan Lesyng
    • 1
    • 4
  • Krystyna Księżopolska-Orłowska
    • 2
  1. 1.Mossakowski Medical Research CentrePolish Academy of SciencesWarsawPoland
  2. 2.Rehabilitation ClinicProfessor E. Reicher National Institute Geriatrics Rheumatology and RehabilitationWarsawPoland
  3. 3.Interdisciplinary Center for Mathematics and Computational ModelingWarsaw UniversityWarsawPoland
  4. 4.Faculty of PhysicsWarsaw UniversityWarsawPoland

Personalised recommendations