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http://repositorio.ufc.br/handle/riufc/73448
Tipo: | Artigo de Periódico |
Título: | Modified squeezeNet architecture for parkinson’s disease detection based on keypress data |
Autor(es): | Bernardo, Lucas Salvador Damaševičius, Robertas Ling, Sai Ho Albuquerque, Victor Hugo Costa de Tavares, João Manuel Ribeiro da Silva |
Palavras-chave: | Parkinson’s disease;Neurodegeneration;Early diagnosis;Key typing;Deep learning;Convolutional network;Mal de Parkinson;Neurodegeneração;Diagnóstico precoce;Digitação de teclas;Aprendizado profundo;Rede convolucional |
Data do documento: | 2022 |
Instituição/Editor/Publicador: | Biomedicines |
Citação: | BERNARDO, Lucas Salvador; DAMAŠEVIČIUS, Robertas; LING, Sai Ho; ALBUQUERQUE, Victor Hugo Costa de; TAVARES, João Manuel Ribeiro da Silva. Modified squeezeNet architecture for parkinson’s disease detection based on keypress data. Biomedicines, [s.l.], v. 10, n. 11, p. 2746, 2022. |
Abstract: | 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 |
URI: | http://www.repositorio.ufc.br/handle/riufc/73448 |
ISSN: | 2227-9059 |
Tipo de Acesso: | Acesso Aberto |
Aparece nas coleções: | DEEL - Artigos publicados em revista científica |
Arquivos associados a este item:
Arquivo | Descrição | Tamanho | Formato | |
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2022_art_lsbernanrdo.pdf | 706,84 kB | Adobe PDF | Visualizar/Abrir |
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