Parkinson’s disease (PD) is a progressive neurodegenerative disorder that causes abnormal movements and an array of other symptoms. An accurate PD diagnosis can be a challenging task as the signs and symptoms, particularly at an early stage, can be similar to other medical conditions or the physiological changes of normal ageing. This work aims to contribute to the PD diagnosis process by using a convolutional neural network, a type of deep neural network architecture, to differentiate between healthy controls and PD patients. Our approach focuses on discovering deviations in patient’s movements with the use of drawing tasks. In addition, this work explores which of two drawing tasks, wire cube or spiral pentagon, are more effective in the discrimination process. With 93:5% accuracy, our convolutional classifier, trained with images of the pentagon drawing task and augmentation techniques, can be used as an objective method to discriminate PD from healthy controls. Our compact model has the potential to be developed into an offline real-time automated single-task diagnostic tool, which can be easily deployed within a clinical setting.

Item Type:	Refereed Article
Keywords:	Parkinson's, movement analysis, artificial intelligence, convolutional neural networks, drawing tasks, deep learning classifier, diagnosis
Research Division:	Biomedical and Clinical Sciences
Research Group:	Neurosciences
Research Field:	Neurosciences not elsewhere classified
Objective Division:	Health
Objective Group:	Clinical health
Objective Field:	Diagnosis of human diseases and conditions
UTAS Author:	Alty, JE (Associate Professor Jane Alty)
ID Code:	146499
Year Published:	2021
Web of Science® Times Cited:	4
Deposited By:	Wicking Dementia Research and Education Centre
Deposited On:	2021-09-09
Last Modified:	2022-08-23
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