Qi Hengnian, Zhang Ruoyu, Wei Zhuqin, Zhang Chu, Wang Lina, Lang Qing, Zhang Kai, Tian Xuesong
Information Engineering Department, Huzhou University, Huzhou, China.
School of Medicine and Nursing, Huzhou University, Huzhou, China.
Front Aging Neurosci. 2023 Mar 15;15:1117250. doi: 10.3389/fnagi.2023.1117250. eCollection 2023.
Alzheimer's disease (AD) has an insidious onset, the early stages are easily overlooked, and there are no reliable, rapid, and inexpensive ancillary detection methods. This study analyzes the differences in handwriting kinematic characteristics between AD patients and normal elderly people to model handwriting characteristics. The aim is to investigate whether handwriting analysis has a promising future in AD auxiliary screening or even auxiliary diagnosis and to provide a basis for developing a handwriting-based diagnostic tool.
Thirty-four AD patients (15 males, 77.15 ± 1.796 years) and 45 healthy controls (20 males, 74.78 ± 2.193 years) were recruited. Participants performed four writing tasks with digital dot-matrix pens which simultaneously captured their handwriting as they wrote. The writing tasks consisted of two graphics tasks and two textual tasks. The two graphics tasks are connecting fixed dots (task 1) and copying intersecting pentagons (task 2), and the two textual tasks are dictating three words (task 3) and copying a sentence (task 4). The data were analyzed by using Student's -test and Mann-Whitney U test to obtain statistically significant handwriting characteristics. Moreover, seven classification algorithms, such as eXtreme Gradient Boosting (XGB) and Logistic Regression (LR) were used to build classification models. Finally, the Receiver Operating Characteristic (ROC) curve, accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and Area Under Curve (AUC) were used to assess whether writing scores and kinematics parameters are diagnostic.
Kinematic analysis showed statistically significant differences between the AD and controlled groups for most parameters ( < 0.05, < 0.01). The results found that patients with AD showed slower writing speed, tremendous writing pressure, and poorer writing stability. We built statistically significant features into a classification model, among which the model built by XGB was the most effective with a maximum accuracy of 96.55%. The handwriting characteristics also achieved good diagnostic value in the ROC analysis. Task 2 had a better classification effect than task 1. ROC curve analysis showed that the best threshold value was 0.084, accuracy = 96.30%, sensitivity = 100%, specificity = 93.41%, PPV = 92.21%, NPV = 100%, and AUC = 0.991. Task 4 had a better classification effect than task 3. ROC curve analysis showed that the best threshold value was 0.597, accuracy = 96.55%, sensitivity = 94.20%, specificity = 98.37%, PPV = 97.81%, NPV = 95.63%, and AUC = 0.994.
This study's results prove that handwriting characteristic analysis is promising in auxiliary AD screening or AD diagnosis.
阿尔茨海默病（AD）起病隐匿，早期易被忽视，且尚无可靠、快速且经济的辅助检测方法。本研究分析AD患者与正常老年人笔迹运动学特征的差异，以建立笔迹特征模型。目的是探讨笔迹分析在AD辅助筛查甚至辅助诊断方面是否具有广阔前景，并为开发基于笔迹的诊断工具提供依据。
招募了34例AD患者（15例男性，年龄77.15±1.796岁）和45例健康对照者（20例男性，年龄74.78±2.193岁）。参与者使用数字点阵笔完成四项书写任务，书写时该笔会同时记录他们的笔迹。书写任务包括两项图形任务和两项文本任务。两项图形任务分别是连接固定点（任务1）和复制相交五边形（任务2），两项文本任务分别是听写三个单词（任务3）和抄写一个句子（任务4）。使用学生t检验和曼-惠特尼U检验对数据进行分析，以获得具有统计学意义的笔迹特征。此外，使用了七种分类算法，如极端梯度提升（XGB）和逻辑回归（LR）来建立分类模型。最后，使用受试者工作特征（ROC）曲线、准确率、敏感性、特异性、阳性预测值（PPV）、阴性预测值（NPV）和曲线下面积（AUC）来评估书写分数和运动学参数是否具有诊断价值。
运动学分析显示，AD组与对照组在大多数参数上存在统计学显著差异（P<0.05，P<0.01）。结果发现，AD患者书写速度较慢、书写压力较大且书写稳定性较差。我们将具有统计学意义的特征纳入分类模型，其中由XGB构建的模型最为有效，最高准确率为96.55%。笔迹特征在ROC分析中也具有良好的诊断价值。任务2的分类效果优于任务1。ROC曲线分析显示，最佳阈值为0.084，准确率=96.30%，敏感性=100%，特异性=93.41%，PPV=92.21%，NPV=100%，AUC=0.991。任务4的分类效果优于任务3。ROC曲线分析显示，最佳阈值为0.597，准确率=96.55%，敏感性=94.20%，特异性=98.37%，PPV=97.81%，NPV=95.63%，AUC=0.994。
本研究结果证明，笔迹特征分析在AD辅助筛查或AD诊断方面具有广阔前景。
