Abstract
Objective: We present the PaHaW Parkinson's disease handwriting database, consisting of handwriting samples from Parkinson's disease (PD) patients and healthy controls. Our goal is to show that kinematic features and pressure features in handwriting can be used for the differential diagnosis of PD. Methods and Material: The database contains records from 37 PD patients and 38 healthy controls performing eight different handwriting tasks. The tasks include drawing an Archimedean spiral, repetitively writing orthographically simple syllables and words, and writing of a sentence. In addition to the conventional kinematic features related to the dynamics of handwriting, we investigated new pressure features based on the pressure exerted on the writing surface. To discriminate between PD patients and healthy subjects, three different classifiers were compared: K-nearest neighbors (K-NN), ensemble AdaBoost classifier, and support vector machines (SVM). Results: For predicting PD based on kinematic and pressure features of handwriting, the best performing model was SVM with classification accuracy of Pacc = 81.3% (sensitivity Psen = 87.4% and specificity of Pspe = 80.9%). When evaluated separately, pressure features proved to be relevant for PD diagnosis, yielding Pacc = 82.5% compared to Pacc = 75.4% using kinematic features. Conclusion: Experimental results showed that an analysis of kinematic and pressure features during handwriting can help assess subtle characteristics of handwriting and discriminate between PD patients and healthy controls.
Abstract (translated)
目标：我们提出了PaHaW帕金森病手写数据库，该数据库包含来自帕金森病（PD）患者和健康对照组的手写样本。我们的目标是展示手写的动力学特征和压力特征可用于帕金森病的鉴别诊断。 方法与材料：该数据库包括37名PD患者和38名健康对照者执行八种不同手写任务的记录。这些任务包括绘制阿基米德螺旋线，反复书写正字法简单的音节和单词，以及书写句子。除了常规的手写动力学特征外，我们还研究了基于对手写表面施加的压力的新压力特征。为了区分PD患者和健康受试者，比较了三种不同的分类器：K最近邻（K-NN）、集成AdaBoost分类器和支持向量机（SVM）。 结果：在根据手写的动力学和压力特征预测帕金森病方面，表现最好的模型是支持向量机（SVM），其分类准确率为Pacc = 81.3%（敏感性Psen = 87.4%，特异性Pspe = 80.9%）。单独评估时，压力特征对于PD诊断具有相关性，得出的准确率为Pacc = 82.5%，而使用动力学特征得到的准确率为Pacc = 75.4%。 结论：实验结果显示，在手写过程中对手写的动力学和压力特征进行分析可以帮助评估手写的细微特性，并区分帕金森病患者与健康对照组。
URL
https://arxiv.org/abs/2411.03044
