Vladimir Džepina

Depressive disorders significantly impact individuals worldwide, including children and adolescents. Despite their widespread occurrence, early and precise diagnosis of depressive disorders remains a complex and challenging task, particularly in younger populations. This study proposes a novel machine learning framework leveraging kinematic handwriting analysis to enhance the detection of psychomotor disturbances indicative of psychomotor retardation in youths with depression. The handwriting data were acquired from 20 youths with depression and 20 healthy controls. All participants completed a simple repetitive handwriting task: continuous writing of the small cursive Latin letter “l”. Segmentation of the handwriting data into individual “Letters” was conducted, and 177 kinematic features were extracted and analyzed. Statistical methods were used to identify significant features. After recursive feature elimination, classification was achieved through machine learning algorithms: logistic regression, support vector machine, and random forest. After the identification of 40 significant features, logistic regression, utilizing an optimal three-feature subset, achieved the highest accuracy in classifying individual letters of 76.7% and the highest accuracy in classifying subjects of 82.5%. The feature selection process revealed that velocity-related features were most effective in distinguishing patients with depression from controls, expectedly reflecting a slowdown in psychomotor functioning among the patients. The findings demonstrate that kinematic handwriting analysis, when combined with machine learning techniques, offers a promising tool to support objective recognition of psychomotor speed, providing insight into psychomotor retardation in youth with depression.