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Stochastic anomaly detection in eye-tracking data for quantification of motor symptoms in Parkinson's disease
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Clinical Neurophysiology.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Neurology.
2013 (English)In: International Symposium on Computational Models for Life Sciences: CMLS 2013, Melville, NY: American Institute of Physics (AIP), 2013, 98-107 p.Conference paper, Published paper (Refereed)
Abstract [en]

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-type model of the smooth pursuit system and attempts to find statistically significant differences between the estimated parameters in healthy controls and patientts with Parkinson's disease. The second method applies the same statistical method to distinguish between the gaze trajectories of healthy and Parkinson subjects attempting to track 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.

Place, publisher, year, edition, pages
Melville, NY: American Institute of Physics (AIP), 2013. 98-107 p.
Series
AIP Conference Proceedings, 1559
Keyword [en]
Smooth pursuit, Anomaly detection, Modeling biomedical systems, Parkinson's Disease
National Category
Neurosciences Control Engineering
Identifiers
URN: urn:nbn:se:uu:diva-221215DOI: 10.1063/1.4825001ISI: 000331467600013ISBN: 978-0-7354-1187-6 (print)OAI: oai:DiVA.org:uu-221215DiVA: diva2:707970
Conference
CMLS 2013, November 27-29, Sydney, Australia
Available from: 2013-10-09 Created: 2014-03-26 Last updated: 2014-03-28Bibliographically approved

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Jansson, DanielMedvedev, AlexanderAxelson, HansNyholm, Dag

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