BACKGROUND Parkinson's disease (PD) affects millions of people worldwide, and it is predicted that this pathology will gravely increase in the next few years. Unfortunately, there's currently no cure for this disease, indeed an early diagnosis of Parkinson's disease can help to better manage its symptoms and its evolution. One of the most frequent abilities and usually also the first manifestation of Parkinson's disease is alteration of handwriting. NEW METHOD We propose a novel method to detect Parkinson's disease, based on the segmentation of the online handwritten text into lines. Indeed, we propose to compare Parkinson's disease patients and healthy controls, based on the full dynamics of new temporal and spectral features. Three classifiers were used, K-Nearest Neighbors, Support Vector Machine and Decision Trees. The performances of these three classifiers were estimated using a stratified nested 10 cross-validation. All the models in this study have been evaluated using classification accuracy, balanced accuracy, sensitivity, specificity, F-Score and Matthews Correlation Coefficient. RESULTS An accuracy of 92.86 % was obtained with Decision Trees classifier in the last line. The new categories of spectral and temporal features gave the best classification performances in comparison to the basic statistical features. COMPARISON WITH EXISTING METHODS Previous studies have only focused on words or sentences. This is the first study to deal with the analysis of a text composed of several lines. CONCLUSION The last line discriminates at best between Parkinson's disease patients and healthy controls. This obtained result has further strengthened our hypothesis concerning the fatigue occurring while writing in PD patients.

中文翻译：

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一种结合阿拉伯在线手写的时态和频谱特征的新颖方法，用于帕金森氏病的预测。

背景技术帕金森氏病（PD）影响全世界数以百万计的人，并且预计这种病理学将在未来几年中严重增加。不幸的是，目前尚无该病的治愈方法，确实，帕金森氏病的早期诊断可以帮助更好地控制其症状和发展。帕金森氏病最常见的能力之一（通常也是帕金森氏病的第一个表现）是笔迹的改变。新方法我们提出了一种基于在线手写文本分割成行的检测帕金森氏病的新方法。实际上，我们建议根据新的时间和频谱特征的完整动态来比较帕金森氏病患者和健康对照者。使用了三个分类器：K最近邻，支持向量机和决策树。这三个分类器的性能使用分层嵌套的10个交叉验证进行估算。本研究中的所有模型均已使用分类准确性，平衡准确性，敏感性，特异性，F得分和马修斯相关系数进行了评估。结果在最后一行，使用决策树分类器获得了92.86％的准确度。与基本统计特征相比，频谱和时间特征的新类别提供了最佳的分类性能。与现有方法的比较先前的研究仅集中于单词或句子。这是处理由几行组成的文本的分析的第一项研究。结论最后一行在帕金森氏病患者和健康对照之间有最好的区别。