Cargando…
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...
Autores principales: | , , , , |
---|---|
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 |
_version_ | 1784836071296598016 |
---|---|
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. |
format | Online Article Text |
id | pubmed-9687688 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT bernardolucassalvador modifiedsqueezenetarchitectureforparkinsonsdiseasedetectionbasedonkeypressdata AT damaseviciusrobertas modifiedsqueezenetarchitectureforparkinsonsdiseasedetectionbasedonkeypressdata AT lingsaiho modifiedsqueezenetarchitectureforparkinsonsdiseasedetectionbasedonkeypressdata AT dealbuquerquevictorhugoc modifiedsqueezenetarchitectureforparkinsonsdiseasedetectionbasedonkeypressdata AT tavaresjoaomanuelrs modifiedsqueezenetarchitectureforparkinsonsdiseasedetectionbasedonkeypressdata |