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

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 moveme...

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Autores principales: Bernardo, Lucas Salvador, Damaševičius, Robertas, Ling, Sai Ho, de Albuquerque, Victor Hugo C., Tavares, João Manuel R. S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9687688/
https://www.ncbi.nlm.nih.gov/pubmed/36359266
http://dx.doi.org/10.3390/biomedicines10112746
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author Bernardo, Lucas Salvador
Damaševičius, Robertas
Ling, Sai Ho
de Albuquerque, Victor Hugo C.
Tavares, João Manuel R. S.
author_facet Bernardo, Lucas Salvador
Damaševičius, Robertas
Ling, Sai Ho
de Albuquerque, Victor Hugo C.
Tavares, João Manuel R. S.
author_sort Bernardo, Lucas Salvador
collection PubMed
description 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.
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spelling pubmed-96876882022-11-25 Modified SqueezeNet Architecture for Parkinson’s Disease Detection Based on Keypress Data Bernardo, Lucas Salvador Damaševičius, Robertas Ling, Sai Ho de Albuquerque, Victor Hugo C. Tavares, João Manuel R. S. Biomedicines Article 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. MDPI 2022-10-28 /pmc/articles/PMC9687688/ /pubmed/36359266 http://dx.doi.org/10.3390/biomedicines10112746 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Bernardo, Lucas Salvador
Damaševičius, Robertas
Ling, Sai Ho
de Albuquerque, Victor Hugo C.
Tavares, João Manuel R. S.
Modified SqueezeNet Architecture for Parkinson’s Disease Detection Based on Keypress Data
title Modified SqueezeNet Architecture for Parkinson’s Disease Detection Based on Keypress Data
title_full Modified SqueezeNet Architecture for Parkinson’s Disease Detection Based on Keypress Data
title_fullStr Modified SqueezeNet Architecture for Parkinson’s Disease Detection Based on Keypress Data
title_full_unstemmed Modified SqueezeNet Architecture for Parkinson’s Disease Detection Based on Keypress Data
title_short Modified SqueezeNet Architecture for Parkinson’s Disease Detection Based on Keypress Data
title_sort modified squeezenet architecture for parkinson’s disease detection based on keypress data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9687688/
https://www.ncbi.nlm.nih.gov/pubmed/36359266
http://dx.doi.org/10.3390/biomedicines10112746
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