Li Chong, Zhang Kunxue, Lin Qunxing, Huang Shan, Cheng Wanying, Lei Yueshiyuan, Zhao Xinyu, Zhao Jiubo
Department of Psychiatry, Zhujiang Hospital, Southern Medical University, No. 253, Industrial Avenue Zhong, Guangzhou, Guangdong, China.
Department of Neurology, Nanfang Hospital, Southern Medical University, No. 1838 Guangzhou Dadao Road North, Guangzhou, Guangdong, China.
BMC Med. 2025 May 13;23(1):282. doi: 10.1186/s12916-025-04101-2.
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.
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.
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.
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.
本研究旨在确定经历抑郁发作的个体的笔迹模式是否发生改变。此外，我们基于心理任务中的电子笔迹开发了一种用于识别重度抑郁症（MDD）的模型。
共有130名患者和117名健康对照完成了21项与心理相关的笔迹任务。电子数位板记录了几个笔迹特征，包括水平和垂直坐标、笔尖压力和速度以及倾斜角度。计算每个笔迹特征的统计指标。进行了包括差异分析在内的统计分析，以确定抑郁症的预测因素。此外，还开发了逻辑回归和机器学习模型来区分重度抑郁症。
该研究纳入了130名初发抑郁症患者（平均（标准差（SD））年龄，20.42（5.21））和117名健康对照（平均（SD）年龄，20.54（2.60））。t检验和逻辑分析结果表明，抑郁症患者的笔迹压力最小值较高，笔迹速度中位数升高，笔尖抖动较大。LightGBM机器学习模型表现出令人满意的性能，平均受试者工作特征曲线下的交叉验证面积为0.90（标准差，0.01）。方差分析显示，与中性和积极任务模型相比，消极问答任务模型表现更优。
本研究表明，抑郁症患者表现出典型的笔迹变化，并开发了一种经济高效、快速且有效的识别重度抑郁症的模型。这一发现为未来开发多模态识别模型奠定了坚实基础。
