Subject: In this article, convolutional networks of one, two, and three dimensions are compared with respect to their ability to distinguish between the drawing tests produced by Parkinson's disease patients and healthy control subjects. Motivation: The application of deep learning techniques for the analysis of drawing tests to support the diagnosis of Parkinson's disease has become a growing trend in the area of Artificial Intelligence. Method: The dynamic features of the handwriting signal are embedded in the static test data to generate one-dimensional time series, two-dimensional RGB images and three-dimensional voxelized point clouds, and then one-, two-, and three-dimensional CNN can be used to automatically extract features for effective diagnosis. Novelty: While there are many results that describe the application of two-dimensional convolutional models to the problem, to the best knowledge of the authors, there are no results based on the application of three-dimensional models and very few using one-dimensional models. Main result: The accuracy of the one-, two- and three-dimensional CNN models was 62.50%, 77.78% and 83.34% in the DraWritePD dataset (acquired by the authors) and 73.33%, 80.00% and 86.67% in the PaHaW dataset (well known from the literature), respectively. For these two data sets, the proposed three-dimensional convolutional classification method exhibits the best diagnostic performance.

翻译：主题：本文比较了一维、二维和三维卷积网络在区分帕金森病患者与健康对照组产生的绘图测试结果方面的能力。动机：应用深度学习技术分析绘图测试以辅助帕金森病诊断已成为人工智能领域的一个重要趋势。方法：将手写信号的动态特征嵌入静态测试数据中，生成一维时间序列、二维RGB图像和三维体素化点云，进而利用一维、二维和三维CNN自动提取特征以实现有效诊断。创新点：尽管已有大量研究描述了二维卷积模型在该问题上的应用，但据作者所知，目前尚无基于三维模型的应用成果，且极少有研究使用一维模型。主要结果：在DraWritePD数据集（作者自行采集）上，一维、二维和三维CNN模型的准确率分别为62.50%、77.78%和83.34%；在文献中广为人知的PaHaW数据集上，准确率分别为73.33%、80.00%和86.67%。针对这两个数据集，所提出的三维卷积分类方法展现出最优的诊断性能。