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Subject: In this article, convolutional networks of one, two, and threedimensions are compared with respect to their ability to distinguish betweenthe drawing tests produced by Parkinson's disease patients and healthy controlsubjects.  Motivation: The application of deep learning techniques for the analysis ofdrawing tests to support the diagnosis of Parkinson's disease has become agrowing trend in the area of Artificial Intelligence.  Method: The dynamic features of the handwriting signal are embedded in thestatic test data to generate one-dimensional time series, two-dimensional RGBimages and three-dimensional voxelized point clouds, and then one-, two-, andthree-dimensional CNN can be used to automatically extract features foreffective diagnosis.  Novelty: While there are many results that describe the application oftwo-dimensional convolutional models to the problem, to the best knowledge ofthe authors, there are no results based on the application of three-dimensionalmodels and very few using one-dimensional models.  Main result: The accuracy of the one-, two- and three-dimensional CNN modelswas 62.50%, 77.78% and 83.34% in the DraWritePD dataset (acquired by theauthors) and 73.33%, 80.00% and 86.67% in the PaHaW dataset (well known fromthe literature), respectively. For these two data sets, the proposedthree-dimensional convolutional classification method exhibits the bestdiagnostic performance.

Comparison of One- Two- and Three- Dimensional CNN models for Drawing-Test-Based Diagnostics of the Parkinson's Disease