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

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


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.


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



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”.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Daniel Jansson
    • 1
    Email author
  • 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

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