Document Detail


Parkinsonian tremor identification with multiple local field potential feature classification.
MedLine Citation:
PMID:  22771289     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
This paper explores the development of multi-feature classification techniques used to identify tremor-related characteristics in the Parkinsonian patient. Local field potentials were recorded from the subthalamic nucleus and the globus pallidus internus of eight Parkinsonian patients through the implanted electrodes of a Deep brain stimulation (DBS) device prior to device internalization. A range of signal processing techniques were evaluated with respect to their tremor detection capability and used as inputs in a multi-feature neural network classifier to identify the activity of Parkinsonian tremor. The results of this study show that a trained multi-feature neural network is able, under certain conditions, to achieve excellent detection accuracy on patients unseen during training. Overall the tremor detection accuracy was mixed, although an accuracy of over 86% was achieved in four out of the eight patients.
Authors:
Eduard Bakstein; Jonathan Burgess; Kevin Warwick; Virginie Ruiz; Tipu Aziz; John Stein
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-7-3
Journal Detail:
Title:  Journal of neuroscience methods     Volume:  -     ISSN:  1872-678X     ISO Abbreviation:  -     Publication Date:  2012 Jul 
Date Detail:
Created Date:  2012-7-9     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  7905558     Medline TA:  J Neurosci Methods     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
Copyright © 2012. Published by Elsevier B.V.
Affiliation:
Department of Cybernetics, Czech Technical University, Prague, Czech Republic.
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