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

Bernardo, LS; Damaševičius, R; Ling, SH; de Albuquerque, VHC; Tavares, JMRS

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

Bernardo, LS Damaševičius, R Ling, SH de Albuquerque, VHC Tavares, JMRS

Permalink

Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Biomedicines, 2022, 10, (11), pp. 2746
Issue Date:: 2022-10-28

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Published versionAdobe PDF (706.84 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Bernardo, LS
dc.contributor.author	Damaševičius, R
dc.contributor.author	Ling, SH
dc.contributor.author	de Albuquerque, VHC
dc.contributor.author	Tavares, JMRS
dc.date.accessioned	2023-04-12T05:19:00Z
dc.date.available	2022-10-21
dc.date.available	2023-04-12T05:19:00Z
dc.date.issued	2022-10-28
dc.identifier.citation	Biomedicines, 2022, 10, (11), pp. 2746
dc.identifier.issn	2227-9059
dc.identifier.issn	2227-9059
dc.identifier.uri	http://hdl.handle.net/10453/169642
dc.description.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.
dc.format	Electronic
dc.language	eng
dc.publisher	MDPI
dc.relation.ispartof	Biomedicines
dc.relation.isbasedon	10.3390/biomedicines10112746
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Modified SqueezeNet Architecture for Parkinson's Disease Detection Based on Keypress Data.
dc.type	Journal Article
utslib.citation.volume	10
utslib.location.activity	Switzerland
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Centre for Health Technologies (CHT)
utslib.copyright.status	open_access	*
dc.date.updated	2023-04-12T05:18:45Z
pubs.issue	11
pubs.publication-status	Published online
pubs.volume	10
utslib.citation.issue	11

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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/169642