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Open AccessFeature PaperArticle

Parkinson’s Disease Detection from Drawing Movements Using Convolutional Neural Networks

Speech Technology Group, Center for Information Processing and Telecommunications, E.T.S.I Telecomunicación, Universidad Politécnica de Madrid, Avenida Complutense, 30. 28040 Madrid, Spain
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Electronics 2019, 8(8), 907; https://doi.org/10.3390/electronics8080907
Received: 1 August 2019 / Revised: 13 August 2019 / Accepted: 14 August 2019 / Published: 17 August 2019
(This article belongs to the Special Issue Recent Advances in Biometrics and its Applications)
Nowadays, an important research effort in healthcare biometrics is finding accurate biomarkers that allow developing medical-decision support tools. These tools help to detect and supervise illnesses like Parkinson’s disease (PD). This paper contributes to this effort by analyzing a convolutional neural network (CNN) for PD detection from drawing movements. This CNN includes two parts: feature extraction (convolutional layers) and classification (fully connected layers). The inputs to the CNN are the module of the Fast Fourier’s transform in the range of frequencies between 0 Hz and 25 Hz. We analyzed the discrimination capability of different directions during drawing movements obtaining the best results for X and Y directions. This analysis was performed using a public dataset: Parkinson Disease Spiral Drawings Using Digitized Graphics Tablet dataset. The best results obtained in this work showed an accuracy of 96.5%, a F1-score of 97.7%, and an area under the curve of 99.2%. View Full-Text
Keywords: Parkinson’s disease detection; drawing movements; deep learning; convolutional neural networks Parkinson’s disease detection; drawing movements; deep learning; convolutional neural networks
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Gil-Martín, M.; Montero, J.M.; San-Segundo, R. Parkinson’s Disease Detection from Drawing Movements Using Convolutional Neural Networks. Electronics 2019, 8, 907.

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