Major depressive disorder recognition based on electronic handwriting recorded in psychological tasks.

Abstract

Background: This study aimed to determine whether handwriting patterns are altered in individuals experiencing depressive episodes. Additionally, we developed a model for the recognition of major depressive disorder (MDD) based on electronic handwriting in psychological tasks.

Methods: A total of 130 patients and 117 healthy controls completed 21 psychology-related handwriting tasks. The electronic tablet recorded several handwriting characteristics, including horizontal and vertical coordinates, nib pressure and speed, and inclination angle. The statistical indicators for each handwriting characteristic were calculated. Statistical analyses, including differential analysis, were performed to identify predictors of depression. Furthermore, logistic regression and machine learning models were developed to discriminate MDD.

Results: The study included 130 patients with onset depression (mean (standard deviation (SD)) age, 20.42 (5.21)) and 117 healthy controls (mean (SD) age, 20.54 (2.60)). The t-test and logistics analysis results indicated that depressed patients exhibited a higher minimum of handwriting pressure, an elevated median of handwriting speed, and greater pen tip jitter. The LightGBM machine learning model exhibited satisfactory performance, with a cross-validated area under the receiver operating curve of mean 0.90 (SD, 0.01). The analysis of variance revealed that the negative question-answer task model exhibited superior performance compared to the neutral and positive task models.

Conclusions: The present study indicates that depressed patients exhibit modal handwriting changes and developed a cost-effective, rapid, and valid model for identifying MDD. This finding established a strong foundation for developing multimodal recognition models in the future.