Parkinson’s Disease tremor classification – a comparison between Support Vector Machines and neural networksPan, S., Iplikci, S., Warwick, K. and Aziz, T. Z. (2012) Parkinson’s Disease tremor classification – a comparison between Support Vector Machines and neural networks. Expert Systems with Applications, 39 (12). pp. 10764-10771. 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.2012.02.189 Abstract/SummaryDeep Brain Stimulation has been used in the study of and for treating Parkinson’s Disease (PD) tremor symptoms since the 1980s. In the research reported here we have carried out a comparative analysis to classify tremor onset based on intraoperative microelectrode recordings of a PD patient’s brain Local Field Potential (LFP) signals. In particular, we compared the performance of a Support Vector Machine (SVM) with two well known artificial neural network classifiers, namely a Multiple Layer Perceptron (MLP) and a Radial Basis Function Network (RBN). The results show that in this study, using specifically PD data, the SVM provided an overall better classification rate achieving an accuracy of 81% recognition.
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