Parkinson's Disease tremor classification - A comparison between Support Vector Machines and neural networks

Authors:
Song Pan;Serdar Iplikci;Kevin Warwick;Tipu Z. Aziz
Affiliations:
School of Systems Engineering, University of Reading, UK;Department of Electrical and Electronics Engineering, Pamukkale University, Turkey;School of Systems Engineering, University of Reading, UK;University Laboratory of Physiology, University of Oxford, UK
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

Deep 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.