Predicting the Onset of Freezing of Gait Using EEG Dynamics

John, AR; Cao, Z; Chen, H-T; Martens, KE; Georgiades, M; Gilat, M; Nguyen, HT; Lewis, SJG; Lin, C-T

Predicting the Onset of Freezing of Gait Using EEG Dynamics

John, AR Cao, Z Chen, H-T Martens, KE Georgiades, M Gilat, M Nguyen, HT Lewis, SJG Lin, C-T

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Applied Sciences, 2022, 13, (1), pp. 302
Issue Date:: 2022-12-27

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Field	Value	Language
dc.contributor.author	John, AR
dc.contributor.author	Cao, Z
dc.contributor.author	Chen, H-T
dc.contributor.author	Martens, KE
dc.contributor.author	Georgiades, M
dc.contributor.author	Gilat, M
dc.contributor.author	Nguyen, HT
dc.contributor.author	Lewis, SJG
dc.contributor.author	Lin, C-T
dc.date.accessioned	2023-01-16T03:40:13Z
dc.date.available	2023-01-16T03:40:13Z
dc.date.issued	2022-12-27
dc.identifier.citation	Applied Sciences, 2022, 13, (1), pp. 302
dc.identifier.issn	2076-3417
dc.identifier.issn	2076-3417
dc.identifier.uri	http://hdl.handle.net/10453/164997
dc.description.abstract	<jats:p>Freezing of gait (FOG) severely incapacitates the mobility of patients with advanced Parkinson’s disease (PD). An accurate prediction of the onset of FOG could improve the quality of life for PD patients. However, it is imperative to distinguish the possibility of the onset of FOG from that of voluntary stopping. Our previous work demonstrated the neurological differences between the transition to FOG and voluntary stopping using electroencephalogram (EEG) signals. We employed a timed up-and-go (TUG) task to elicit FOG in PD patients. Some of these TUG tasks had an additional voluntary stopping component, where participants stopped walking based on verbal instruction to “stop”. The performance of the convolutional neural network (CNN) in identifying the transition to FOG from normal walking and the transition to voluntary stopping was explored. To the best of our knowledge, this work is the first study to propose a deep learning method to distinguish the transition to FOG from the transition to voluntary stop in PD patients. The models, trained on the EEG data from 17 PD patients who manifested FOG episodes, considering a short two-second transition window for FOG occurrence or voluntary stopping, achieved close to 75% classification accuracy in distinguishing transition to FOG from the transition to voluntary stopping or normal walking. Our results represent an important step toward advanced EEG-based cueing systems for smart FOG intervention, excluding the potential confounding of voluntary stopping.</jats:p>
dc.language	en
dc.publisher	MDPI AG
dc.relation	http://purl.org/au-research/grants/arc/DP180100656
dc.relation	http://purl.org/au-research/grants/arc/DP210101093
dc.relation	http://purl.org/au-research/grants/arc/DP180100670
dc.relation.ispartof	Applied Sciences
dc.relation.isbasedon	10.3390/app13010302
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Predicting the Onset of Freezing of Gait Using EEG Dynamics
dc.type	Journal Article
utslib.citation.volume	13
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 - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2023-01-16T03:40:10Z
pubs.issue	1
pubs.publication-status	Published online
pubs.volume	13
utslib.citation.issue	1

Abstract:

Freezing of gait (FOG) severely incapacitates the mobility of patients with advanced Parkinson’s disease (PD). An accurate prediction of the onset of FOG could improve the quality of life for PD patients. However, it is imperative to distinguish the possibility of the onset of FOG from that of voluntary stopping. Our previous work demonstrated the neurological differences between the transition to FOG and voluntary stopping using electroencephalogram (EEG) signals. We employed a timed up-and-go (TUG) task to elicit FOG in PD patients. Some of these TUG tasks had an additional voluntary stopping component, where participants stopped walking based on verbal instruction to “stop”. The performance of the convolutional neural network (CNN) in identifying the transition to FOG from normal walking and the transition to voluntary stopping was explored. To the best of our knowledge, this work is the first study to propose a deep learning method to distinguish the transition to FOG from the transition to voluntary stop in PD patients. The models, trained on the EEG data from 17 PD patients who manifested FOG episodes, considering a short two-second transition window for FOG occurrence or voluntary stopping, achieved close to 75% classification accuracy in distinguishing transition to FOG from the transition to voluntary stopping or normal walking. Our results represent an important step toward advanced EEG-based cueing systems for smart FOG intervention, excluding the potential confounding of voluntary stopping.

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http://hdl.handle.net/10453/164997