Stochastic Anomaly Detection in Eye-Tracking Data for Quantification of Motor Symptoms in Parkinson’s Disease

  • Daniel Jansson
  • Alexander Medvedev
  • Hans Axelson
  • Dag Nyholm
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 823)

Abstract

Two methods for distinguishing between healthy controls and patients diagnosed with Parkinson’s disease by means of recorded smooth pursuit eye movements are presented and evaluated. Both methods are based on the principles of stochastic anomaly detection and make use of orthogonal series approximation for probability distribution estimation. The first method relies on the identification of a Wiener model of the smooth pursuit system and attempts to find statistically significant differences between the estimated parameters in healthy controls and patients with Parkinson’s disease. The second method applies the same statistical method to distinguish between the gaze trajectories of healthy and Parkinson subjects tracking visual stimuli. Both methods show promising results, where healthy controls and patients with Parkinson’s disease are effectively separated in terms of the considered metric. The results are preliminary because of the small number of participating test subjects, but they are indicative of the potential of the presented methods as diagnosing or staging tools for Parkinson’s disease.

Keywords

Smooth pursuit Parkinson’s disease Parametric modeling Nonparametric modeling Visual stimulus design Eye tracking 

Notes

Acknowledgements

This chapter is in part financed by Advanced Grant 247035 from European Research Council entitled “Systems and Signals Tools for Estimation and Analysis of Mathematical Models in Endocrinology and Neurology”.

References

  1. 1.
    G. Avanzini, F. Girotti, T. Carazeni, R. Spreafico, Oculomotor disorders in Huntington’s chorea. J. Neurol. Neurosurg. Psychiatry 42, 581–589 (1979)CrossRefGoogle Scholar
  2. 2.
    R. Bednarik, T. Kinnunen, A. Mihaila, P. Fränti, Eye-Movements as a Biometric, in Image Analysis, ed. by H. Kalviainen, J. Parkkinen, A. Kaarna. Lecture Notes in Computer Science, vol. 3540 (Springer, Berlin/Heidelberg, 2005), pp. 780–789Google Scholar
  3. 3.
    R. Dodge, Five types of eye movements in the horizontal meridian plane of the field of regard. Am. J. Physiol. 8, 307–329 (1903)Google Scholar
  4. 4.
    J.R. Fienup, Phase retrieval algorithms: a comparison. Appl. Opt. 21, 2758–2769 (1982)CrossRefGoogle Scholar
  5. 5.
    J.M. Gibson, R. Pimlott, C. Kennard, Ocular motor and manual tracking in Parkinson’s disease and the effect of treatment. J. Neurol. 50, 853–860 (1987)Google Scholar
  6. 6.
    H. He, J. Li, P. Stoica, Waveform Design for Active Sensing Systems – A Computational Approach (Camebridge University Press, New York, 2011)Google Scholar
  7. 7.
    D. Jansson, A. Medvedev, Dynamic smooth pursuit gain estimation from eye-tracking data, in IEEE Conference on Decision and Control, Orlando (2011)Google Scholar
  8. 8.
    D. Jansson, A. Medvedev, Visual stimulus design in parameter estimation of the human smooth pursuit system from eye-tracking data, in IEEE American Control Conference, Washington D.C. (2013)Google Scholar
  9. 9.
    D. Jansson, A. Medvedev, H.W. Axelson, Mathematical modeling and grey-box identification of the human smooth pursuit mechanism, in IEEE Multi-conference on Systems and Control, Yokohama, (2010)Google Scholar
  10. 10.
    D. Jansson, A. Medvedev, H.W. Axelson, D. Nyholm, Stochastic anomaly detection in eye-tracking data for quantification of motor symptoms in Parkinson’s disease, in International Symposium on Computational Models for Life Sciences, Sydney, vol. 1559, November 2013, pp. 98–107Google Scholar
  11. 11.
    D. Jansson, O. Rosén, A. Medvedev, Non-parametric analysis of eye-tracking data by anomaly detection, in IEEE European Control Conference, Zürich (2013)Google Scholar
  12. 12.
    E.R. Kandel, J.H. Schwartz, T.M. Jessell, Principles of Neural Science (McGraw Hill, New York, 2000)Google Scholar
  13. 13.
    P. Kasprowski, Eye movements in biometrics, in Biometric Authentication, ed. by D. Maltoni, A.K. Jain. Lecture Notes in Computer Science 3087 (Springer, Berlin/Heidelberg, 2004), pp. 248–258Google Scholar
  14. 14.
    N. Kathmann, A. Hochrein, R. Uwer, B. Bondy, Deficits in gain of smooth pursuit eye movements in Schizophrenia and affective disorder patients and their unaffected relatives. Am. J. Psychiatry 160, 696–702 (2003)CrossRefGoogle Scholar
  15. 15.
    T.H. Koornwinder, R. Wong, R. Koekoek, R. Swarttouw, Orthogonal polynomials, in NIST Handbook of Mathematical Functions (Camebridge University Press, Cambridge/New York, 2010). ISBN 978-0521192255Google Scholar
  16. 16.
    S. Marino, E. Sessam, G. Di Lorenzo, P. Lanzafame, G. Scullica, A. Bramanti, F. La Rosa, G. Iannizzotto, P. Bramanti, P. Di Bella, Quantitative analysis of pursuit ocular movements in Parkinson’s disease by using a video-based eye-tracking system. Eur. Neurol. 58, 193–197 (2007)CrossRefGoogle Scholar
  17. 17.
    C.H. Meyer, A.G. Lasker, D.A. Robinson, The upper limit of human smooth pursuit velocity. Vis. Res. 25, 561–563 (1985)CrossRefGoogle Scholar
  18. 18.
    T. Nakamura, R. Kanayama, R. Sano, M. Ohki, Y. Kimura, M. Aoyagi, Y. Koike, Quantitative analysis of ocular movements in Parkinson’s disease. Acta Oto-Iaryngologica 111, 559–562 (1991)CrossRefGoogle Scholar
  19. 19.
    W.T. Newsome, R.H. Wurtz, M.R. Dürsteler, A. Mikami, Deficits in visual motion processing following ibotenic acid lesions of the middle temporal visual area of macaque monkey. J. Neurosci. 5, 825–840 (1985)Google Scholar
  20. 20.
    C. Ramaker, J. Marinus, A.M. Stiggelbout, B.J. van Hilten, Systematic evaluation of rating scales for impairment and disability in Parkinson’s disease. Mov. Disord. 17(5), 867–876 (2002)CrossRefGoogle Scholar
  21. 21.
    C. Rashbass, The relationship between saccadic and smooth tracking eye movements. J. Physiol. 159, 326–338 (1961)Google Scholar
  22. 22.
    S.C. Schwartz, Estimation of probability density by an orthogonal series. Ann. Math. Stat. 38, 1261–1265 (1967)CrossRefMATHGoogle Scholar
  23. 23.
    M. Tarter, R. Kronmal, On multivariate density estimates based on orthogonal expansions. Ann. Math. Stat. 41, 718–722 (1970)CrossRefMATHMathSciNetGoogle Scholar
  24. 24.
    J.R. Thompson, P.R.A. Tapia, Non-parametric Function Estimation, Modeling & Simulation. Misc. Bks. (Society for Industrial and Applied Mathematics, Philadelphia, 1990)Google Scholar
  25. 25.
    J.A. Tropp, I.S. Dhillon, R.W. Heath, T. Strohmer, Designing structured tight frames via an alternating projection method. IEEE Trans. Inf. Theory 51, 188–209 (2005)CrossRefMATHMathSciNetGoogle Scholar
  26. 26.
    O.B. White, J.A. Saint-Cyr, R.D. Tomlinson, J.A. Sharpe, Ocular motor deficits in Parkinson’s disease, II. Control of the saccadic and smoothp ursuit systems. Oxf. J. Med. Brain 106, 571–587 (1983)Google Scholar
  27. 27.
    T. Wigren, MATLAB software for recursive identification of Wiener systems. Systems and Control, Department of Information Technology, Uppsala University (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Daniel Jansson
    • 1
  • Alexander Medvedev
    • 1
  • Hans Axelson
    • 2
  • Dag Nyholm
    • 3
  1. 1.Department of Information TechnologyUppsala UniversityUppsalaSweden
  2. 2.Department of Neuroscience, NeurophysiologyUppsala UniversityUppsalaSweden
  3. 3.Department of Neuroscience, NeurologyUppsala UniversityUppsalaSweden

Personalised recommendations