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Derivation and Analysis of Dynamic Handwriting Features as Clinical Markers of Parkinson’s Disease

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Title:Derivation and Analysis of Dynamic Handwriting Features as Clinical Markers of Parkinson’s Disease
Authors:Poon, Christine
Gorji, Niku
Latt, Mark
Tsoi, Kelvin
Choi, Boris
show 2 moreLoy, Clement
Poon, Simon
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Keywords:Big Data on Healthcare Application
Information Technology in Healthcare
Handwriting Analysis
Parkinson’s Disease
Medical Screening
show 2 morefeature expansion and selection
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Date Issued:08 Jan 2019
Abstract:Parkinson’s Disease (PD) is a complex neurodegenerative disorder that is challenging to diagnose. Recent research has demonstrated predictive value in the analysis of dynamic handwriting features for detecting PD, however, consensus on clinically-useful features is yet to be reached. Here we explore and evaluate secondary kinematic handwriting features hypothesized to be diagnostically relevant to Parkinson’s Disease using a publicly-available Spiral Drawing Test PD dataset. Univariate and multivariate analysis was performed on derived features. Classification outcome was determined using logistic regression models with 10-fold cross validation. Feature correlation was based on model specificity and sensitivity. Variations in grip angle, instantaneous acceleration and pressure indices were found to have high predictive potential as clinical markers of PD, with combined classification accuracy of above 90%. Our results show that the significance of secondary handwriting features and recommend the feature expansion step for hypothesis generation, comparative evaluation of test types and improved classification accuracy.
Pages/Duration:10 pages
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections: Big Data on Healthcare Application

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