Deep Learning-aided Parkinson's Disease Diagnosis from Handwritten Dynamics

Author(s)

Pereira, Clayton R. ; Weber, Silke A. T. ; Hook, Christian ; Rosa, Gustavo H. ; Papa, Joao ; IEEE

• Document type: Journal article
• Source: 2016 29TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI); v. N/A, p. 7-pg., 2016-01-01.

Abstract

Parkinson's Disease (PD) automatic identification in early stages is one of the most challenging medicine-related tasks to date, since a patient may have a similar behaviour to that of a healthy individual at the very early stage of the disease. In this work, we cope with PD automatic identification by means of a Convolutional Neural Network (CNN), which aims at learning features from a signal extracted during the individual's exam by means of a smart pen composed of a series of sensors that can extract information from handwritten dynamics. We have shown CNNs are able to learn relevant information, thus outperforming results obtained from raw data. Also, this work aimed at building a public dataset to be used by researchers worldwide in order to foster PD-related research. (AU)

• FAPESP's process: 15/25739-4 - On the Study of Semantics in Deep Learning Models
• Grantee: Gustavo Henrique de Rosa
• Support Opportunities: Scholarships in Brazil - Master
• FAPESP's process: 14/16250-9 - On the parameter optimization in machine learning techniques: advances and paradigms
• Grantee: João Paulo Papa
• Support Opportunities: Regular Research Grants
• FAPESP's process: 10/15566-1 - Evaluation of mathematic modell for study of fine motor function in individuals with Parkinson's disease using a multi-sensor biometric smart pen, BiSP
• Grantee: Silke Anna Theresa Weber
• Support Opportunities: Regular Research Grants