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

• Autor(a) principal: Lucas Salvador Bernardo
• Data de Publicação: 2022
• Outros Autores: Robertas Damaševicius, Sai Ho Ling, Victor Hugo C. de Albuquerque, João Manuel R. S. Tavares
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://hdl.handle.net/10216/145978
• topic: Ciências Tecnológicas, Ciências médicas e da saúde; Technological sciences, Medical and Health sciences

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.