The detection of Freezing of Gait in Parkinson's disease patients using EEG signals based on Wavelet decomposition

Handojoseno, AMA; Shine, JM; Nguyen, TN; Tran, Y; Lewis, SJG; Nguyen, HT

The detection of Freezing of Gait in Parkinson's disease patients using EEG signals based on Wavelet decomposition

Handojoseno, AMA Shine, JM Nguyen, TN Tran, Y Lewis, SJG Nguyen, HT

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Publisher:: IEEE Xplore
Publication Type:: Conference Proceeding
Citation:: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2012, pp. 69 - 72
Issue Date:: 2012-12-14

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Field	Value	Language
dc.contributor.author	Handojoseno, AMA	en_US
dc.contributor.author	Shine, JM	en_US
dc.contributor.author	Nguyen, TN	en_US
dc.contributor.author	Tran, Y	en_US
dc.contributor.author	Lewis, SJG	en_US
dc.contributor.author	Nguyen, HT	en_US
dc.date	2012-08-28	en_US
dc.date.issued	2012-12-14	en_US
dc.identifier.citation	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2012, pp. 69 - 72	en_US
dc.identifier.isbn	9781424441198	en_US
dc.identifier.issn	1557-170X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119556
dc.description.abstract	Freezing of Gait (FOG) is one of the most disabling gait disturbances of Parkinson's disease (PD). The experience has often been described as feeling like their feet have been glued to the floor while trying to walk and as such it is a common cause of falling in PD patients. In this paper, EEG subbands Wavelet Energy and Total Wavelet Entropy were extracted using the multiresolution decomposition of EEG signal based on the Discrete Wavelet Transform and were used to analyze the dynamics in the EEG during freezing. The Back Propagation Neural Network classifier has the ability to identify the onset of freezing of PD patients during walking using these features with average values of accuracy, sensitivity and specificity are around 75 %. This results have proved the feasibility of utilized EEG in future treatment of FOG. © 2012 IEEE.	en_US
dc.publisher	IEEE Xplore	en_US
dc.relation.ispartof	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS	en_US
dc.relation.ispartof	International Conference of the IEEE Engineering in Medicine and Biology Society	en_US
dc.relation.isbasedon	10.1109/EMBC.2012.6345873	en_US
dc.rights	© 2012 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	The detection of Freezing of Gait in Parkinson's disease patients using EEG signals based on Wavelet decomposition	en_US
dc.type	Conference Proceeding
utslib.location.activity	San Diego, CA, USA	en_US
utslib.for	090303 Biomedical Instrumentation	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/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.declined	2017-11-01T10:17:47.352+1100
pubs.consider-herdc	false	en_US
pubs.deleted	2017-11-01T10:17:47.352+1100
pubs.finish-date	2012-09-01	en_US
pubs.publication-status	Published	en_US
pubs.start-date	2012-08-28	en_US

Abstract:

Freezing of Gait (FOG) is one of the most disabling gait disturbances of Parkinson's disease (PD). The experience has often been described as feeling like their feet have been glued to the floor while trying to walk and as such it is a common cause of falling in PD patients. In this paper, EEG subbands Wavelet Energy and Total Wavelet Entropy were extracted using the multiresolution decomposition of EEG signal based on the Discrete Wavelet Transform and were used to analyze the dynamics in the EEG during freezing. The Back Propagation Neural Network classifier has the ability to identify the onset of freezing of PD patients during walking using these features with average values of accuracy, sensitivity and specificity are around 75 %. This results have proved the feasibility of utilized EEG in future treatment of FOG. © 2012 IEEE.

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http://hdl.handle.net/10453/119556