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Prediction of Parkinson’s disease tremor onset using radial basis function neural networks

Wu, D., Warwick, K., Ma, Z., Burgess, J. G., Pan, S. and Aziz, T. Z. (2010) Prediction of Parkinson’s disease tremor onset using radial basis function neural networks. Expert Systems with Applications, 37 (4). pp. 2923-2928. ISSN 0957-4174

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To link to this article DOI: 10.1016/j.eswa.2009.09.045

Abstract/Summary

The possibility of using a radial basis function neural network (RBFNN) to accurately recognise and predict the onset of Parkinson’s disease tremors in human subjects is discussed in this paper. The data for training the RBFNN are obtained by means of deep brain electrodes implanted in a Parkinson disease patient’s brain. The effectiveness of a RBFNN is initially demonstrated by a real case study.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Systems Engineering
ID Code:17004
Uncontrolled Keywords:Parkinson’s disease; Radial basis function neural network; Deep brain implantation
Publisher:Elsevier

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