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Title: Dynamically enhanced static handwriting representation for Parkinson's disease detection
Authors: Diaz, Moises 
Ferrer, Miguel Angel 
Impedovo, Donato
Pirlo, Giuseppe
Vessio, Gennaro
UNESCO Clasification: 3314 Tecnología médica
320507 Neurología
Keywords: Computer Aided Diagnosis
Convolutional Neural Networks
Dynamically Enhanced Static Handwriting
Parkinson'S Disease
Issue Date: 2019
Journal: Pattern Recognition Letters 
Abstract: Computer aided diagnosis systems can provide non-invasive, low-cost tools to support clinicians. These systems have the potential to assist the diagnosis and monitoring of neurodegenerative disorders, in particular Parkinson's disease (PD). Handwriting plays a special role in the context of PD assessment. In this paper, the discriminating power of “dynamically enhanced” static images of handwriting is investigated. The enhanced images are synthetically generated by exploiting simultaneously the static and dynamic properties of handwriting. Specifically, we propose a static representation that embeds dynamic information based on: (i) drawing the points of the samples, instead of linking them, so as to retain temporal/velocity information; and (ii) adding pen-ups for the same purpose. To evaluate the effectiveness of the new handwriting representation, a fair comparison between this approach and state-of-the-art methods based on static and dynamic handwriting is conducted on the same dataset, i.e. PaHaW. The classification workflow employs transfer learning to extract meaningful features from multiple representations of the input data. An ensemble of different classifiers is used to achieve the final predictions. Dynamically enhanced static handwriting is able to outperform the results obtained by using static and dynamic handwriting separately.
ISSN: 0167-8655
DOI: 10.1016/j.patrec.2019.08.018
Source: Pattern Recognition Letters [ISSN 0167-8655], v. 128, p. 204-210
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