Efficient diagnosis system for Parkinson's disease using deep belief network

Al-Fatlawi, AH; Jabardi, MH; Ling, SH

Efficient diagnosis system for Parkinson's disease using deep belief network

Al-Fatlawi, AH Jabardi, MH Ling, SH

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Evolutionary Computation (CEC), 2016 IEEE Congress on, 2016, pp. 1324 - 1330
Issue Date:: 2016

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Field	Value	Language
dc.contributor.author	Al-Fatlawi, AH	en_US
dc.contributor.author	Jabardi, MH	en_US
dc.contributor.author	Ling, SH	en_US
dc.date	2016-07-24	en_US
dc.date.issued	2016	en_US
dc.identifier.citation	Evolutionary Computation (CEC), 2016 IEEE Congress on, 2016, pp. 1324 - 1330	en_US
dc.identifier.isbn	978-1-5090-0623-6	en_US
dc.identifier.uri	http://hdl.handle.net/10453/118071
dc.description.abstract	In this paper, a deep belief network (DBN) has been adopted as an efficient technique to diagnosis the Parkinson's disease (PD). This diagnosis has been established based on the speech signal of the patients. Through the distinguishing and analyzing of the speech signal, the DBN has the ability to diagnose Parkinson's disease. To realize the diagnosis of Parkinson's disease by using DBN, the proposed system has been trained and tested with voices from a number of patients and healthy people. A feature extraction process has been prepared to be inputted to the deep belief network (DBN) which is used to create a template matching of the voices. In this paper, DBN is used to classify the Parkinson's disease which composes two stacked Restricted Boltzmann Machines (RBMs) and one output layer. Two stages of learning need to be applied to optimize the networks' parameters. The first stage is unsupervised learning which uses RBMs to overcome the problem that can cause because of the random value of the initial weights. Secondly, backpropagation algorithm is used as a supervised learning for the fine tuning. To illustrate the effectiveness of the proposed system, the experimental results are compared with different approaches and related works. The overall testing accuracy of the proposed system is 94% which is better than all of the compared methods. In short, the DBN is an effective method to diagnosis Parkinson's disease by using the speech signal.	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	Evolutionary Computation (CEC), 2016 IEEE Congress on	en_US
dc.relation.ispartof	IEEE Congress on Evolutionary Computation	en_US
dc.relation.isbasedon	10.1109/CEC.2016.7743941	en_US
dc.relation.isreplacedby	10453/122134
dc.relation.isreplacedby	http://hdl.handle.net/10453/122134
dc.rights	© 2016 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.rights	info:eu-repo/semantics/openAccess
dc.title	Efficient diagnosis system for Parkinson's disease using deep belief network	en_US
dc.type	Conference Proceeding
utslib.location	Piscataway, USA	en_US
utslib.for	090302 Biomechanical Engineering	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	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	2017-10-03T13:36:05.785+1100
pubs.consider-herdc	true	en_US
pubs.merge-to	10453/122134
pubs.merge-to	http://hdl.handle.net/10453/122134
pubs.deleted	2017-10-03T13:36:05.785+1100
pubs.finish-date	2016-07-29	en_US
pubs.place-of-publication	Piscataway, USA	en_US
pubs.publication-status	Published	en_US
pubs.start-date	2016-07-24	en_US

Abstract:

In this paper, a deep belief network (DBN) has been adopted as an efficient technique to diagnosis the Parkinson's disease (PD). This diagnosis has been established based on the speech signal of the patients. Through the distinguishing and analyzing of the speech signal, the DBN has the ability to diagnose Parkinson's disease. To realize the diagnosis of Parkinson's disease by using DBN, the proposed system has been trained and tested with voices from a number of patients and healthy people. A feature extraction process has been prepared to be inputted to the deep belief network (DBN) which is used to create a template matching of the voices. In this paper, DBN is used to classify the Parkinson's disease which composes two stacked Restricted Boltzmann Machines (RBMs) and one output layer. Two stages of learning need to be applied to optimize the networks' parameters. The first stage is unsupervised learning which uses RBMs to overcome the problem that can cause because of the random value of the initial weights. Secondly, backpropagation algorithm is used as a supervised learning for the fine tuning. To illustrate the effectiveness of the proposed system, the experimental results are compared with different approaches and related works. The overall testing accuracy of the proposed system is 94% which is better than all of the compared methods. In short, the DBN is an effective method to diagnosis Parkinson's disease by using the speech signal.

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