Prodromal Diagnosis of Lewy Body Diseases Based on the Assessment of
Graphomotor and Handwriting Difficulties
- URL: http://arxiv.org/abs/2301.08534v1
- Date: Fri, 20 Jan 2023 12:30:28 GMT
- Title: Prodromal Diagnosis of Lewy Body Diseases Based on the Assessment of
Graphomotor and Handwriting Difficulties
- Authors: Zoltan Galaz, Jiri Mekyska, Jan Mucha, Vojtech Zvoncak, Zdenek Smekal,
Marcos Faundez-Zanuy, Lubos Brabenec, Ivona Moravkova, Irena Rektorova
- Abstract summary: We trained classification models for each task separately as well as a model for their combination to estimate the predictive power of the features for the identification of LBDs.
Using this approach we were able to identify prodromal LBDs with 74% accuracy and showed the promising potential of computerized objective and non-invasive diagnosis of LBDs.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: To this date, studies focusing on the prodromal diagnosis of Lewy body
diseases (LBDs) based on quantitative analysis of graphomotor and handwriting
difficulties are missing. In this work, we enrolled 18 subjects diagnosed with
possible or probable mild cognitive impairment with Lewy bodies (MCI-LB), 7
subjects having more than 50% probability of developing Parkinson's disease
(PD), 21 subjects with both possible/probable MCI-LB and probability of PD >
50%, and 37 age- and gender-matched healthy controls (HC). Each participant
performed three tasks: Archimedean spiral drawing (to quantify graphomotor
difficulties), sentence writing task (to quantify handwriting difficulties),
and pentagon copying test (to quantify cognitive decline). Next, we
parameterized the acquired data by various temporal, kinematic, dynamic,
spatial, and task-specific features. And finally, we trained classification
models for each task separately as well as a model for their combination to
estimate the predictive power of the features for the identification of LBDs.
Using this approach we were able to identify prodromal LBDs with 74% accuracy
and showed the promising potential of computerized objective and non-invasive
diagnosis of LBDs based on the assessment of graphomotor and handwriting
difficulties.
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