Abstract
In this paper we evaluate the suitability of handwriting patterns as potential biomarkers to model Parkinson disease (PD). Although the study of PD is attracting the interest of many researchers around the world, databases to evaluate handwriting patterns are scarce and knowledge about patterns associated to PD is limited and biased to the existing datasets. This paper introduces a database with a total of 935 handwriting tasks collected from 55 PD patients and 94 healthy controls (45 young and 49 old). Three feature sets are extracted from the signals: neuromotor, kinematic, and nonlinear dynamic. Different classifiers are used to discriminate between PD and healthy subjects: support vector machines, knearest neighbors, and a multilayer perceptron. The proposed features and classifiers enable to detect PD with accuracies between 81% and 97%. Additionally, new insights are presented on the utility of the studied features for monitoring and detecting PD.
Abstract (translated)
本文评价了书写模式作为潜在生物标志物对帕金森病（PD）模型的适用性。虽然对PD的研究吸引了世界各地的许多研究者的兴趣，但评估手写模式的数据库却十分稀缺，对与PD相关的模式的了解也有限，并且偏向于现有的数据集。本文介绍了一个数据库，共收集了55名PD患者和94名健康对照者（45岁和49岁）的935个笔迹任务。从信号中提取三个特征集：神经运动、运动学和非线性动力学。不同的分类器用于区分PD和健康对象：支持向量机、Knearest邻居和多层感知器。所提出的特征和分类器能够检测出准确度在81%到97%之间的局部放电。此外，还对所研究特征在局部放电监测中的应用提出了新的见解。
URL
https://arxiv.org/abs/1903.08226
