Document Detail


Stochastic Anomaly Detection in Eye-Tracking Data for Quantification of Motor Symptoms in Parkinson's Disease.
MedLine Citation:
PMID:  25381102     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
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.
Authors:
Daniel Jansson; Alexander Medvedev; Hans Axelson; Dag Nyholm
Publication Detail:
Type:  JOURNAL ARTICLE    
Journal Detail:
Title:  Advances in experimental medicine and biology     Volume:  823     ISSN:  0065-2598     ISO Abbreviation:  Adv. Exp. Med. Biol.     Publication Date:  2015  
Date Detail:
Created Date:  2014-11-8     Completed Date:  -     Revised Date:  2014-11-9    
Medline Journal Info:
Nlm Unique ID:  0121103     Medline TA:  Adv Exp Med Biol     Country:  -    
Other Details:
Languages:  ENG     Pagination:  63-82     Citation Subset:  -    
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