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https://hdl.handle.net/11499/8477
Title: | Parkinson's Disease tremor classification - A comparison between Support Vector Machines and neural networks | Authors: | Pan, S. İplikçi, Serdar Warwick, K. Aziz, T.Z. |
Keywords: | Deep Brain Stimulation Intraoperative microelectrode recordings Multiple Layer Perception Parkinson's Disease Radial Basis Neural Network Support Vector Machine Deep brain stimulation Microelectrode recording Multiple layers Parkinson's disease Radial basis neural networks Electrophysiology Microelectrodes Neural networks Neurodegenerative diseases Radial basis function networks Surgery Support vector machines |
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. © 2012 Elsevier Ltd. All rights reserved. | URI: | https://hdl.handle.net/11499/8477 https://doi.org/10.1016/j.eswa.2012.02.189 |
ISSN: | 0957-4174 |
Appears in Collections: | Mühendislik Fakültesi Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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