Please use this identifier to cite or link to this item: 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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