Modified SqueezeNet Architecture for Parkinson's Disease Detection Based on Keypress Data

• Tipo: Artigo em Revista Científica Internacional
• Data: 2022-11

Autores

Lucas Salvador Bernardo (Autor) Outra; Robertas Dama¨evičius (Autor) Outra; Sai Ho Ling (Autor) Outra; Victor Hugo C. de Albuquerque (Autor) Outra; João Manuel R. S. Tavares (Autor) FEUP

Revista

Título: Biomedicines

Vol. 10

Páginas: 2746-2746

Editora: MDPI

Indexação

ISI Web of Knowledge - 0 Citações

ISI Web of Science

Scopus

Clarivate Analytics

Classificação Científica

CORDIS: Ciências Tecnológicas

FOS: Ciências médicas e da saúde

Outras Informações

ID Authenticus: P-00X-GN5

DOI: 10.3390/biomedicines10112746

Abstract (EN): Parkinson's disease (PD) is the most common form of Parkinsonism, which is a group of neurological disorders with PD-like motor impairments. The disease affects over 6 million people worldwide and is characterized by motor and non-motor symptoms. The affected person has trouble in controlling movements, which may affect simple daily-life tasks, such as typing on a computer. We propose the application of a modified SqueezeNet convolutional neural network (CNN) for detecting PD based on the subject's key-typing patterns. First, the data are pre-processed using data standardization and the Synthetic Minority Oversampling Technique (SMOTE), and then a Continuous Wavelet Transformation is applied to generate spectrograms used for training and testing a modified SqueezeNet model. The modified SqueezeNet model achieved an accuracy of 90%, representing a noticeable improvement in comparison to other approaches.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 15