BACKGROUND Handwriting represents one of the major symptom in Parkinson's Disease (PD) patients. The computer-aided analysis of the handwriting allows for the identification of promising patterns that might be useful in PD detection and rating. In this study, we propose an innovative set of features extracted by geometrical, dynamical and muscle activation signals acquired during handwriting tasks, and evaluate the contribution of such features in detecting and rating PD by means of artificial neural networks. METHODS Eleven healthy subjects and twenty-one PD patients were enrolled in this study. Each involved subject was asked to write three different patterns on a graphic tablet while wearing the Myo Armband used to collect the muscle activation signals of the main forearm muscles. We have then extracted several features related to the written pattern, the movement of the pen and the pressure exerted with the pen and the muscle activations. The computed features have been used to classify healthy subjects versus PD patients and to discriminate mild PD patients from moderate PD patients by using an artificial neural network (ANN). RESULTS After the training and evaluation of different ANN topologies, the obtained results showed that the proposed features have high relevance in PD detection and rating. In particular, we found that our approach both detect and rate (mild and moderate PD) with a classification accuracy higher than 90%. CONCLUSIONS In this paper we have investigated the representativeness of a set of proposed features related to handwriting tasks in PD detection and rating. In particular, we used an ANN to classify healthy subjects and PD patients (PD detection), and to classify mild and moderate PD patients (PD rating). The implemented and tested methods showed promising results proven by the high level of accuracy, sensitivity and specificity. Such results suggest the usability of the proposed setup in clinical settings to support the medical decision about Parkinson's Disease.

中文翻译：

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生物特征笔迹分析，以支持帕金森氏病评估和分级。

背景技术手写代表帕金森氏病（PD）患者的主要症状之一。笔迹的计算机辅助分析可以识别出可能有用的模式，这些模式可能在PD检测和评级中很有用。在这项研究中，我们提出了一组创新的特征集，这些特征集是通过手写任务期间获取的几何，动态和肌肉激活信号提取的，并通过人工神经网络评估了这些特征在检测和评估PD中的作用。方法本研究纳入了11名健康受试者和21名PD患者。要求每个参与的受试者戴着Myo臂章来在图形输入板上书写三种不同的图案，该臂章用于收集前臂主要肌肉的肌肉激活信号。然后，我们提取了一些与书写模式，笔的移动以及笔所施加的压力和肌肉激活有关的特征。计算出的特征已用于通过使用人工神经网络（ANN）将健康受试者与PD患者进行分类，并将轻度PD患者与中度PD患者区分开。结果在对不同的人工神经网络拓扑进行训练和评估后，所获得的结果表明，所提出的特征与局部放电的检测和评价具有较高的相关性。尤其是，我们发现我们的方法可以检测和评估（轻度和中度PD），并且分类精度高于90％。结论在本文中，我们研究了与PD检测和评级中的手写任务相关的一组建议功能的代表性。特别是，我们使用ANN对健康受试者和PD患者进行分类（PD检测），并对轻度和中度PD患者进行分类（PD评分）。已实施和经过测试的方法显示出令人鼓舞的结果，其准确性，敏感性和特异性高。这样的结果表明所提出的装置在临床环境中的可用性，以支持有关帕金森氏病的医学决定。