Detection of Gait Initiation Failure in Parkinson’s Disease based on Wavelet Transform and Support Vector Machine

Ly, QT; Handojoseno, AMA; Gilat, M; Chai, R; Marten, KAE; Georgiades, M; Naik, GR; Tran, Y; Lewis, SJG; Nguyen, HT

Detection of Gait Initiation Failure in Parkinson’s Disease based on Wavelet Transform and Support Vector Machine

Ly, QT Handojoseno, AMA Gilat, M Chai, R

Marten, KAE Georgiades, M Naik, GR Tran, Y Lewis, SJG Nguyen, HT

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17), 2017, pp. 3048 - 3051
Issue Date:: 2017-07-11

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Field	Value	Language
dc.contributor.author	Ly, QT	en_US
dc.contributor.author	Handojoseno, AMA	en_US
dc.contributor.author	Gilat, M	en_US
dc.contributor.author	Chai, R https://orcid.org/0000-0002-1922-7024	en_US
dc.contributor.author	Marten, KAE	en_US
dc.contributor.author	Georgiades, M	en_US
dc.contributor.author	Naik, GR	en_US
dc.contributor.author	Tran, Y	en_US
dc.contributor.author	Lewis, SJG	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.date	2017-07-11	en_US
dc.date.issued	2017-07-11	en_US
dc.identifier.citation	Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17), 2017, pp. 3048 - 3051	en_US
dc.identifier.isbn	978-1-5090-2809-2	en_US
dc.identifier.uri	http://hdl.handle.net/10453/115467
dc.description.abstract	Gait initiation Failure (GIF) is the situation in which patients with Parkinson’s disease (PD) feel as if their feet get “stuck” to the floor when initiating their first steps. GIF is a subtype of Freezing of Gait (FOG) and often leads to falls and related injuries. Understanding of neurobiological mechanisms underlying GIF has been limited by difficulties in eliciting and objectively characterizing such gait phenomena in the clinical setting. Studies investigating the effects of GIF on brain activity using EEG offer the potential to study such behavior. In this preliminary study, we present a novel methodology where wavelet transform was used for feature extraction and Support Vector Machine for classifying GIF events in five patients with PD and FOG. To deal with the large amount of EEG data, a Principal Component Analysis (PCA) was applied to reduce the data dimension from 15 EEG channels into 6 principal components (PCs), retaining 93% of the information. Independent Component Analysis using Entropy Bound Minimization (ICA-EBM) was applied to 6 PCs for source separation with the aim of improving detection ability of GIF events as compared to the normal initiation of gait (Good Starts). The results of this analysis demonstrated the correct identification of GIF episodes with an 83.1% sensitivity, 89.5% specificity and 86.3% accuracy. These results suggest that our proposed methodology is a promising non-invasive approach to improve GIF detection in PD and FOG.	en_US
dc.publisher	IEEE	en_US
dc.relation	http://purl.org/au-research/grants/nhmrc/APP1062319
dc.relation	http://purl.org/au-research/grants/arc/DP150102493
dc.relation.ispartof	Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17)	en_US
dc.relation.ispartof	39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17)	en_US
dc.relation.isreplacedby	10453/126110
dc.relation.isreplacedby	http://hdl.handle.net/10453/126110
dc.rights	© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	Detection of Gait Initiation Failure in Parkinson’s Disease based on Wavelet Transform and Support Vector Machine	en_US
dc.type	Conference Proceeding
utslib.location.activity	Jeju Island, Korea	en_US
pubs.embargo.period	Not known	en_US
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 Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.declined	2017-08-18T21:00:00.494+1000
pubs.consider-herdc	true	en_US
pubs.merge-to	10453/126110
pubs.merge-to	http://hdl.handle.net/10453/126110
pubs.deleted	2017-08-18T21:00:00.494+1000
pubs.finish-date	2017-07-15	en_US
pubs.publication-status	Published	en_US
pubs.start-date	2017-07-11	en_US

Abstract:

Gait initiation Failure (GIF) is the situation in which patients with Parkinson’s disease (PD) feel as if their feet get “stuck” to the floor when initiating their first steps. GIF is a subtype of Freezing of Gait (FOG) and often leads to falls and related injuries. Understanding of neurobiological mechanisms underlying GIF has been limited by difficulties in eliciting and objectively characterizing such gait phenomena in the clinical setting. Studies investigating the effects of GIF on brain activity using EEG offer the potential to study such behavior. In this preliminary study, we present a novel methodology where wavelet transform was used for feature extraction and Support Vector Machine for classifying GIF events in five patients with PD and FOG. To deal with the large amount of EEG data, a Principal Component Analysis (PCA) was applied to reduce the data dimension from 15 EEG channels into 6 principal components (PCs), retaining 93% of the information. Independent Component Analysis using Entropy Bound Minimization (ICA-EBM) was applied to 6 PCs for source separation with the aim of improving detection ability of GIF events as compared to the normal initiation of gait (Good Starts). The results of this analysis demonstrated the correct identification of GIF episodes with an 83.1% sensitivity, 89.5% specificity and 86.3% accuracy. These results suggest that our proposed methodology is a promising non-invasive approach to improve GIF detection in PD and FOG.

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http://hdl.handle.net/10453/115467