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

Autor(es)

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

• Tipo de documento: Artigo Científico
• Fonte: 2016 29TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI); v. N/A, p. 7-pg., 2016-01-01.

Resumo

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)

• Processo FAPESP: 15/25739-4 - Estudo de Semântica em Modelos de Aprendizado em Profundidade
• Beneficiário: Gustavo Henrique de Rosa
• Modalidade de apoio: Bolsas no Brasil - Mestrado
• Processo FAPESP: 14/16250-9 - Sobre a otimização de parâmetros em técnicas de aprendizado de máquina: avanços e paradigmas
• Beneficiário: João Paulo Papa
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 10/15566-1 - Estudo da aplicação de um modelo matemático da avaliação do padrão de motricidade fina em pacientes com doença de Parkinson através do biosensor smart pen BiSP
• Beneficiário: Silke Anna Theresa Weber
• Modalidade de apoio: Auxílio à Pesquisa - Regular