GaborPDNet: Gabor Transformation and Deep Neural Network for Parkinson’s Disease Detection Using EEG Signals

Loh, HW; Ooi, CP; Palmer, E; Barua, PD; Dogan, S; Tuncer, T; Baygin, M; Acharya, UR

GaborPDNet: Gabor Transformation and Deep Neural Network for Parkinson’s Disease Detection Using EEG Signals

Loh, HW Ooi, CP Palmer, E Barua, PD Dogan, S Tuncer, T Baygin, M Acharya, UR

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Electronics, 10, (14), pp. 1740-1740

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Field	Value	Language
dc.contributor.author	Loh, HW
dc.contributor.author	Ooi, CP
dc.contributor.author	Palmer, E
dc.contributor.author	Barua, PD
dc.contributor.author	Dogan, S
dc.contributor.author	Tuncer, T
dc.contributor.author	Baygin, M
dc.contributor.author	Acharya, UR
dc.date.accessioned	2022-03-14T01:17:30Z
dc.date.available	2022-03-14T01:17:30Z
dc.identifier.citation	Electronics, 10, (14), pp. 1740-1740
dc.identifier.issn	2079-9292
dc.identifier.uri	http://hdl.handle.net/10453/155195
dc.description.abstract	<jats:p>Parkinson’s disease (PD) is globally the most common neurodegenerative movement disorder. It is characterized by a loss of dopaminergic neurons in the substantia nigra of the brain. However, current methods to diagnose PD on the basis of clinical features of Parkinsonism may lead to misdiagnoses. Hence, noninvasive methods such as electroencephalographic (EEG) recordings of PD patients can be an alternative biomarker. In this study, a deep-learning model is proposed for automated PD diagnosis. EEG recordings of 16 healthy controls and 15 PD patients were used for analysis. Using Gabor transform, EEG recordings were converted into spectrograms, which were used to train the proposed two-dimensional convolutional neural network (2D-CNN) model. As a result, the proposed model achieved high classification accuracy of 99.46% (±0.73) for 3-class classification (healthy controls, and PD patients with and without medication) using tenfold cross-validation. This indicates the potential of proposed model to simultaneously automatically detect PD patients and their medication status. The proposed model is ready to be validated with a larger database before implementation as a computer-aided diagnostic (CAD) tool for clinical-decision support.</jats:p>
dc.language	en
dc.publisher	MDPI AG
dc.relation.ispartof	Electronics
dc.relation.isbasedon	10.3390/electronics10141740
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.title	GaborPDNet: Gabor Transformation and Deep Neural Network for Parkinson’s Disease Detection Using EEG Signals
dc.type	Journal Article
utslib.citation.volume	10
utslib.for	0906 Electrical and Electronic Engineering
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/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
utslib.copyright.status	open_access	*
dc.date.updated	2022-03-14T01:17:26Z
pubs.issue	14
pubs.publication-status	Published online
pubs.volume	10
utslib.citation.issue	14

Abstract:

Parkinson’s disease (PD) is globally the most common neurodegenerative movement disorder. It is characterized by a loss of dopaminergic neurons in the substantia nigra of the brain. However, current methods to diagnose PD on the basis of clinical features of Parkinsonism may lead to misdiagnoses. Hence, noninvasive methods such as electroencephalographic (EEG) recordings of PD patients can be an alternative biomarker. In this study, a deep-learning model is proposed for automated PD diagnosis. EEG recordings of 16 healthy controls and 15 PD patients were used for analysis. Using Gabor transform, EEG recordings were converted into spectrograms, which were used to train the proposed two-dimensional convolutional neural network (2D-CNN) model. As a result, the proposed model achieved high classification accuracy of 99.46% (±0.73) for 3-class classification (healthy controls, and PD patients with and without medication) using tenfold cross-validation. This indicates the potential of proposed model to simultaneously automatically detect PD patients and their medication status. The proposed model is ready to be validated with a larger database before implementation as a computer-aided diagnostic (CAD) tool for clinical-decision support.

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http://hdl.handle.net/10453/155195