Discriminative Power of Handwriting and Drawing Features in Depression

This study contributes knowledge on the detection of depression through handwriting/drawing features, to identify quantitative and noninvasive indicators of the disorder for implementing algorithms for its automatic detection. For this purpose, an original approach was adopted to provide a dynamic e...

Full description

Saved in:
Bibliographic Details
Published inInternational journal of neural systems Vol. 34; no. 2; p. 2350069
Main Authors Greco, Claudia, Raimo, Gennaro, Amorese, Terry, Cuciniello, Marialucia, Mcconvey, Gavin, Cordasco, Gennaro, Faundez-Zanuy, Marcos, Vinciarelli, Alessandro, Callejas-Carrion, Zoraida, Esposito, Anna
Format Journal Article
LanguageEnglish
Published Singapore 01.02.2024
Subjects
Online AccessGet more information

Cover

Loading…
More Information
Summary:This study contributes knowledge on the detection of depression through handwriting/drawing features, to identify quantitative and noninvasive indicators of the disorder for implementing algorithms for its automatic detection. For this purpose, an original approach was adopted to provide a dynamic evaluation of handwriting/drawing performance of healthy participants with no history of any psychiatric disorders ([Formula: see text]), and patients with a clinical diagnosis of depression ([Formula: see text]). Both groups were asked to complete seven tasks requiring either the writing or drawing on a paper while five handwriting/drawing features' categories (i.e. pressure on the paper, time, ductus, space among characters, and pen inclination) were recorded by using a digitalized tablet. The collected records were statistically analyzed. Results showed that, except for pressure, all the considered features, successfully discriminate between depressed and nondepressed subjects. In addition, it was observed that depression affects different writing/drawing functionalities. These findings suggest the adoption of writing/drawing tasks in the clinical practice as tools to support the current depression detection methods. This would have important repercussions on reducing the diagnostic times and treatment formulation.
ISSN:1793-6462
DOI:10.1142/S0129065723500697