@article{01HP4P9W4BCJVW4XDMR5J9DVG3,
  abstract     = {{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.

Methods: 
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 59.38%, 77.73% and 82.34% in the DraWritePD dataset (acquired by the authors) and 63.33%, 81.33% and 82.22% 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.}},
  articleno    = {{105436}},
  author       = {{Wang, Xuechao and Huang, Junqing and Chatzakou, Marianna and Nõmm, Sven and Valla, Elli and Medijainen, Kadri and Taba, Pille and Toomela, Aaro and Ruzhansky, Michael}},
  issn         = {{1746-8094}},
  journal      = {{BIOMEDICAL SIGNAL PROCESSING AND CONTROL}},
  keywords     = {{Health Informatics,Signal Processing,Biomedical Engineering,Ghent Analysis & PDE center,Parkinson’s disease,Drawing test,Artificial intelligence,Decision support system,Deep learning models,Convolutional neural networks,CNN}},
  language     = {{eng}},
  number       = {{Part B}},
  pages        = {{8}},
  title        = {{Comparison of one- two- and three-dimensional CNN models for drawing-test-based diagnostics of the Parkinson’s disease}},
  url          = {{http://doi.org/10.1016/j.bspc.2023.105436}},
  volume       = {{87}},
  year         = {{2024}},
}

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