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