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Parkinsonian tremor identification with multiple local field potential feature classification

Bakstein, E., Burgess, J., Warwick, K., Ruiz, V., Aziz, T. and Stein, J. (2012) Parkinsonian tremor identification with multiple local field potential feature classification. Journal of Neuroscience Methods, 209 (2). pp. 320-330. ISSN 0165-0270

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To link to this item DOI: 10.1016/j.jneumeth.2012.06.027

Abstract/Summary

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.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science
ID Code:31749
Uncontrolled Keywords:Parkinson’s disease; Local field potentials; Deep brain stimulation; Multiple features; Feature extraction; Neural networks
Publisher:Elsevier

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