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

• Published in: Electronics (Basel) Vol. 8; no. 8; p. 907
• Main Authors: Gil-Martín, Manuel, Montero, Juan Manuel, San-Segundo, Rubén
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2019
• Subjects: Accuracy; Algorithms; Alzheimer's disease; Artificial neural networks; Biomarkers; Biometrics; Classification; Datasets; Digitization; Feature extraction; Handwriting; Kinematics; Medical research; Neural networks; Parkinson's disease; Patients; Writing
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics8080907

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%.