Prediction of Parkinson’s disease tremor onset using radial basis function neural networksWu, 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 Full text not archived in this repository. It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1016/j.eswa.2009.09.045 Abstract/SummaryThe 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.
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