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Signal and Image Analysis for Biomedical and Life Sciences pp 63–82Cite as

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

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Part of the book series: Advances in Experimental Medicine and Biology ((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.

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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”.

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Authors and Affiliations

  1. Department of Information Technology, Uppsala University, Uppsala, Sweden

    Daniel Jansson & Alexander Medvedev

  2. Department of Neuroscience, Neurophysiology, Uppsala University, Uppsala, Sweden

    Hans Axelson

  3. Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden

    Dag Nyholm

Authors
  1. Daniel Jansson
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  2. Alexander Medvedev
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  3. Hans Axelson
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  4. Dag Nyholm
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Corresponding author

Correspondence to Daniel Jansson .

Editor information

Editors and Affiliations

  1. Digital Productivity Flagship, CSIRO, Sydney, New South Wales, Australia

    Changming Sun

  2. Digital Productivity Flagship, CSIRO, Sydney, New South Wales, Australia

    Tomasz Bednarz

  3. The University of Aizu, Fukushima, Japan

    Tuan D. Pham

  4. Digital Productivity Flagship, CSIRO, Sydney, New South Wales, Australia

    Pascal Vallotton

  5. Digital Productivity Flagship, CSIRO, Sydney, New South Wales, Australia

    Dadong Wang

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Jansson, D., Medvedev, A., Axelson, H., Nyholm, D. (2015). Stochastic Anomaly Detection in Eye-Tracking Data for Quantification of Motor Symptoms in Parkinson’s Disease. In: Sun, C., Bednarz, T., Pham, T., Vallotton, P., Wang, D. (eds) Signal and Image Analysis for Biomedical and Life Sciences. Advances in Experimental Medicine and Biology, vol 823. Springer, Cham. https://doi.org/10.1007/978-3-319-10984-8_4

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