Accessibility navigation


Parkinson’s Disease tremor classification – a comparison between Support Vector Machines and neural networks

Pan, 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. ISSN 0957-4174 (In Press)

Full text not archived in this repository.

To link to this article DOI: 10.1016/j.eswa.2012.02.189

Abstract/Summary

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.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Systems Engineering
ID Code:27843
Uncontrolled Keywords:Parkinson’s Disease; Deep Brain Stimulation; Intraoperative microelectrode recordings; Radial Basis Neural Network; Multiple Layer Perception; Support Vector Machine
Publisher:Elsevier

Centaur Editors: Update this record

Page navigation